{"run_id": "codex:add-feature:gpt-5.6-sol:trial1", "ts_iso": "2026-07-18T22:33:43", "harness": "codex", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 143.124, "t_env_setup_s": 0.013, "t_agent_s": 143.124, "t_checker_s": 0.234, "tokens": 45907, "tokens_input_uncached": 41635, "tokens_cache_read": 142080, "tokens_cache_write": null, "tokens_output": 4272, "tokens_reasoning": 1007, "usage_raw": {"input_tokens": 183715, "cached_input_tokens": 142080, "output_tokens": 4272, "reasoning_output_tokens": 1007}, "token_basis": "estimated", "tokens_proxy_input_uncached": 41635, "tokens_proxy_cache_read": 142080, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 4272, "tokens_proxy_reasoning": 1007, "tokens_proxy_calls": 10, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 45907, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_add-feature_w7od24ug", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 2784, "mtime": 1784428536.615984, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1554, "mtime": 1784428536.5384307, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:add-feature:gpt-5.6-sol:trial2", "ts_iso": "2026-07-18T22:36:07", "harness": "codex", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 368.711, "t_env_setup_s": 0.003, "t_agent_s": 368.711, "t_checker_s": 0.228, "tokens": 80525, "tokens_input_uncached": 70626, "tokens_cache_read": 648960, "tokens_cache_write": null, "tokens_output": 9899, "tokens_reasoning": 3226, "usage_raw": {"input_tokens": 719586, "cached_input_tokens": 648960, "output_tokens": 9899, "reasoning_output_tokens": 3226}, "token_basis": "estimated", "tokens_proxy_input_uncached": 133245, "tokens_proxy_cache_read": 1014528, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 17481, "tokens_proxy_reasoning": 7203, "tokens_proxy_calls": 53, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 80525, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_add-feature_y2jfu25r", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/cyC_0ePmaFI5LSZJJJdYVFon/codex/backend-api/codex\"", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3428, "mtime": 1784428874.0492933, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 2272, "mtime": 1784428903.9631279, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:add-feature:gpt-5.6-sol:trial3", "ts_iso": "2026-07-18T22:42:16", "harness": "codex", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 127.142, "t_env_setup_s": 0.003, "t_agent_s": 127.142, "t_checker_s": 0.233, "tokens": 22378, "tokens_input_uncached": 18028, "tokens_cache_read": 146176, "tokens_cache_write": null, "tokens_output": 4350, "tokens_reasoning": 1322, "usage_raw": {"input_tokens": 164204, "cached_input_tokens": 146176, "output_tokens": 4350, "reasoning_output_tokens": 1322}, "token_basis": "estimated", "tokens_proxy_input_uncached": 18028, "tokens_proxy_cache_read": 146176, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 4350, "tokens_proxy_reasoning": 1322, "tokens_proxy_calls": 9, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 22378, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_add-feature_rhcnxi50", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 2806, "mtime": 1784429005.4784732, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1494, "mtime": 1784429005.4022431, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:build-a-cli:gpt-5.6-sol:trial1", "ts_iso": "2026-07-18T22:44:24", "harness": "codex", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 73.769, "t_env_setup_s": 0.001, "t_agent_s": 73.769, "t_checker_s": 0.28, "tokens": 25492, "tokens_input_uncached": 23291, "tokens_cache_read": 71424, "tokens_cache_write": null, "tokens_output": 2201, "tokens_reasoning": 548, "usage_raw": {"input_tokens": 94715, "cached_input_tokens": 71424, "output_tokens": 2201, "reasoning_output_tokens": 548}, "token_basis": "estimated", "tokens_proxy_input_uncached": 23291, "tokens_proxy_cache_read": 71424, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2201, "tokens_proxy_reasoning": 548, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 25492, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_build-a-cli_5a5yt5a3", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/DxpvJEvmLwM_97FUY_vM3RQF/codex/backend-api/codex\"", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 980, "mtime": 1784429099.3578444, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:build-a-cli:gpt-5.6-sol:trial2", "ts_iso": "2026-07-18T22:45:38", "harness": "codex", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 68.628, "t_env_setup_s": 0.0, "t_agent_s": 68.628, "t_checker_s": 0.224, "tokens": 29239, "tokens_input_uncached": 27398, "tokens_cache_read": 65024, "tokens_cache_write": null, "tokens_output": 1841, "tokens_reasoning": 386, "usage_raw": {"input_tokens": 92422, "cached_input_tokens": 65024, "output_tokens": 1841, "reasoning_output_tokens": 386}, "token_basis": "estimated", "tokens_proxy_input_uncached": 27398, "tokens_proxy_cache_read": 65024, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1841, "tokens_proxy_reasoning": 386, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 29239, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_build-a-cli_dqqpussj", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 787, "mtime": 1784429174.2084384, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:build-a-cli:gpt-5.6-sol:trial3", "ts_iso": "2026-07-18T22:46:47", "harness": "codex", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 76.683, "t_env_setup_s": 0.0, "t_agent_s": 76.683, "t_checker_s": 0.239, "tokens": 29532, "tokens_input_uncached": 27504, "tokens_cache_read": 66304, "tokens_cache_write": null, "tokens_output": 2028, "tokens_reasoning": 633, "usage_raw": {"input_tokens": 93808, "cached_input_tokens": 66304, "output_tokens": 2028, "reasoning_output_tokens": 633}, "token_basis": "estimated", "tokens_proxy_input_uncached": 27504, "tokens_proxy_cache_read": 66304, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2028, "tokens_proxy_reasoning": 633, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 29532, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_build-a-cli_8xwcastl", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 956, "mtime": 1784429249.3120632, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:fix-failing-test:gpt-5.6-sol:trial1", "ts_iso": "2026-07-18T22:48:04", "harness": "codex", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 47.38, "t_env_setup_s": 0.002, "t_agent_s": 47.38, "t_checker_s": 0.235, "tokens": 19706, "tokens_input_uncached": 18755, "tokens_cache_read": 71424, "tokens_cache_write": null, "tokens_output": 951, "tokens_reasoning": 65, "usage_raw": {"input_tokens": 90179, "cached_input_tokens": 71424, "output_tokens": 951, "reasoning_output_tokens": 65}, "token_basis": "estimated", "tokens_proxy_input_uncached": 18755, "tokens_proxy_cache_read": 71424, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 951, "tokens_proxy_reasoning": 65, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 19706, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_fix-failing-test_7tpn93h_", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784429319.695183, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:fix-failing-test:gpt-5.6-sol:trial2", "ts_iso": "2026-07-18T22:48:52", "harness": "codex", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 46.797, "t_env_setup_s": 0.001, "t_agent_s": 46.797, "t_checker_s": 0.234, "tokens": 8454, "tokens_input_uncached": 7545, "tokens_cache_read": 67328, "tokens_cache_write": null, "tokens_output": 909, "tokens_reasoning": 28, "usage_raw": {"input_tokens": 74873, "cached_input_tokens": 67328, "output_tokens": 909, "reasoning_output_tokens": 28}, "token_basis": "estimated", "tokens_proxy_input_uncached": 7545, "tokens_proxy_cache_read": 67328, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 909, "tokens_proxy_reasoning": 28, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8454, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_fix-failing-test_nagxy_h4", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784429366.8626993, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:fix-failing-test:gpt-5.6-sol:trial3", "ts_iso": "2026-07-18T22:49:39", "harness": "codex", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 36.966, "t_env_setup_s": 0.001, "t_agent_s": 36.966, "t_checker_s": 0.237, "tokens": 8326, "tokens_input_uncached": 7518, "tokens_cache_read": 67328, "tokens_cache_write": null, "tokens_output": 808, "tokens_reasoning": 52, "usage_raw": {"input_tokens": 74846, "cached_input_tokens": 67328, "output_tokens": 808, "reasoning_output_tokens": 52}, "token_basis": "estimated", "tokens_proxy_input_uncached": 7518, "tokens_proxy_cache_read": 67328, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 808, "tokens_proxy_reasoning": 52, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8326, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_fix-failing-test_shvsj916", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/49-QU5tXdJzAtvdTYpUlGbIV/codex/backend-api/codex\"", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784429405.1917741, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:make-ci-green:gpt-5.6-sol:trial1", "ts_iso": "2026-07-18T22:50:16", "harness": "codex", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 138.189, "t_env_setup_s": 0.005, "t_agent_s": 138.189, "t_checker_s": 0.234, "tokens": 35022, "tokens_input_uncached": 32380, "tokens_cache_read": 269568, "tokens_cache_write": null, "tokens_output": 2642, "tokens_reasoning": 331, "usage_raw": {"input_tokens": 301948, "cached_input_tokens": 269568, "output_tokens": 2642, "reasoning_output_tokens": 331}, "token_basis": "estimated", "tokens_proxy_input_uncached": 89606, "tokens_proxy_cache_read": 540416, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5895, "tokens_proxy_reasoning": 1130, "tokens_proxy_calls": 34, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 35022, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_make-ci-green_ci5lsjsk", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/7zpETd0phUZjTd0b_5AD4tre/codex/backend-api/codex\"", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784429459.7663898, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784429459.8417244, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784429459.917176, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784429547.732583, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784429460.0707386, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784429460.1467612, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:make-ci-green:gpt-5.6-sol:trial2", "ts_iso": "2026-07-18T22:52:35", "harness": "codex", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 97.263, "t_env_setup_s": 0.003, "t_agent_s": 97.263, "t_checker_s": 0.236, "tokens": 28906, "tokens_input_uncached": 26177, "tokens_cache_read": 140032, "tokens_cache_write": null, "tokens_output": 2729, "tokens_reasoning": 516, "usage_raw": {"input_tokens": 166209, "cached_input_tokens": 140032, "output_tokens": 2729, "reasoning_output_tokens": 516}, "token_basis": "estimated", "tokens_proxy_input_uncached": 26177, "tokens_proxy_cache_read": 140032, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2729, "tokens_proxy_reasoning": 516, "tokens_proxy_calls": 9, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 28906, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_make-ci-green_ve0opz9n", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/-jPn0IejKE0fwmB8jq7NWYA_/codex/backend-api/codex\"", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784429618.076789, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784429618.1517942, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784429618.2260554, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784429618.3011482, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784429618.376056, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784429618.4518142, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:make-ci-green:gpt-5.6-sol:trial3", "ts_iso": "2026-07-18T22:54:12", "harness": "codex", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 102.018, "t_env_setup_s": 0.003, "t_agent_s": 102.018, "t_checker_s": 0.238, "tokens": 16251, "tokens_input_uncached": 14117, "tokens_cache_read": 135168, "tokens_cache_write": null, "tokens_output": 2134, "tokens_reasoning": 491, "usage_raw": {"input_tokens": 149285, "cached_input_tokens": 135168, "output_tokens": 2134, "reasoning_output_tokens": 491}, "token_basis": "estimated", "tokens_proxy_input_uncached": 14117, "tokens_proxy_cache_read": 135168, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2134, "tokens_proxy_reasoning": 491, "tokens_proxy_calls": 8, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 16251, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_make-ci-green_ns30eg58", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784429717.0235357, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784429717.098089, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784429717.172666, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784429717.2470489, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784429717.3230383, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784429717.3980062, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:make-it-run:gpt-5.6-sol:trial1", "ts_iso": "2026-07-18T22:55:55", "harness": "codex", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 36.956, "t_env_setup_s": 0.002, "t_agent_s": 36.956, "t_checker_s": 0.238, "tokens": 18554, "tokens_input_uncached": 17685, "tokens_cache_read": 56320, "tokens_cache_write": null, "tokens_output": 869, "tokens_reasoning": 61, "usage_raw": {"input_tokens": 74005, "cached_input_tokens": 56320, "output_tokens": 869, "reasoning_output_tokens": 61}, "token_basis": "estimated", "tokens_proxy_input_uncached": 17685, "tokens_proxy_cache_read": 56320, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 869, "tokens_proxy_reasoning": 61, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 18554, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_make-it-run_4g1vfxyj", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784429780.5370743, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784429780.4612517, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:make-it-run:gpt-5.6-sol:trial2", "ts_iso": "2026-07-18T22:56:32", "harness": "codex", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 41.399, "t_env_setup_s": 0.001, "t_agent_s": 41.399, "t_checker_s": 0.239, "tokens": 24755, "tokens_input_uncached": 23692, "tokens_cache_read": 66304, "tokens_cache_write": null, "tokens_output": 1063, "tokens_reasoning": 67, "usage_raw": {"input_tokens": 89996, "cached_input_tokens": 66304, "output_tokens": 1063, "reasoning_output_tokens": 67}, "token_basis": "estimated", "tokens_proxy_input_uncached": 23692, "tokens_proxy_cache_read": 66304, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1063, "tokens_proxy_reasoning": 67, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 24755, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_make-it-run_d7rsqdbj", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784429819.8643396, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784429819.7881408, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:make-it-run:gpt-5.6-sol:trial3", "ts_iso": "2026-07-18T22:57:14", "harness": "codex", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 44.724, "t_env_setup_s": 0.001, "t_agent_s": 44.724, "t_checker_s": 0.242, "tokens": 27734, "tokens_input_uncached": 26903, "tokens_cache_read": 47104, "tokens_cache_write": null, "tokens_output": 831, "tokens_reasoning": 22, "usage_raw": {"input_tokens": 74007, "cached_input_tokens": 47104, "output_tokens": 831, "reasoning_output_tokens": 22}, "token_basis": "estimated", "tokens_proxy_input_uncached": 26903, "tokens_proxy_cache_read": 47104, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 831, "tokens_proxy_reasoning": 22, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 27734, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_make-it-run_qf6c9v48", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/6x3FAS_cCbRdOdbSvLE5kTO5/codex/backend-api/codex\"", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784429868.7691765, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784429868.6933413, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:misleading-error:gpt-5.6-sol:trial1", "ts_iso": "2026-07-18T22:57:59", "harness": "codex", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 58.682, "t_env_setup_s": 0.003, "t_agent_s": 58.682, "t_checker_s": 0.236, "tokens": 11341, "tokens_input_uncached": 9954, "tokens_cache_read": 81408, "tokens_cache_write": null, "tokens_output": 1387, "tokens_reasoning": 409, "usage_raw": {"input_tokens": 91362, "cached_input_tokens": 81408, "output_tokens": 1387, "reasoning_output_tokens": 409}, "token_basis": "estimated", "tokens_proxy_input_uncached": 9954, "tokens_proxy_cache_read": 81408, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1387, "tokens_proxy_reasoning": 409, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 11341, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_misleading-error_wxufg5pb", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784429916.7529812, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:misleading-error:gpt-5.6-sol:trial2", "ts_iso": "2026-07-18T22:58:58", "harness": "codex", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 39.096, "t_env_setup_s": 0.002, "t_agent_s": 39.096, "t_checker_s": 0.236, "tokens": 21699, "tokens_input_uncached": 20795, "tokens_cache_read": 54272, "tokens_cache_write": null, "tokens_output": 904, "tokens_reasoning": 53, "usage_raw": {"input_tokens": 75067, "cached_input_tokens": 54272, "output_tokens": 904, "reasoning_output_tokens": 53}, "token_basis": "estimated", "tokens_proxy_input_uncached": 20795, "tokens_proxy_cache_read": 54272, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 904, "tokens_proxy_reasoning": 53, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 21699, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_misleading-error_3iehjkwu", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784429968.0188112, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:misleading-error:gpt-5.6-sol:trial3", "ts_iso": "2026-07-18T22:59:38", "harness": "codex", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 39.405, "t_env_setup_s": 0.002, "t_agent_s": 39.405, "t_checker_s": 0.243, "tokens": 8662, "tokens_input_uncached": 7742, "tokens_cache_read": 67328, "tokens_cache_write": null, "tokens_output": 920, "tokens_reasoning": 56, "usage_raw": {"input_tokens": 75070, "cached_input_tokens": 67328, "output_tokens": 920, "reasoning_output_tokens": 56}, "token_basis": "estimated", "tokens_proxy_input_uncached": 7742, "tokens_proxy_cache_read": 67328, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 920, "tokens_proxy_reasoning": 56, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8662, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_misleading-error_lwh_dw6b", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784430005.9103816, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:taskflow:gpt-5.6-sol:trial1", "ts_iso": "2026-07-18T23:00:17", "harness": "codex", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 122.583, "t_env_setup_s": 0.012, "t_agent_s": 122.583, "t_checker_s": 0.234, "tokens": 45961, "tokens_input_uncached": 42820, "tokens_cache_read": 416256, "tokens_cache_write": null, "tokens_output": 3141, "tokens_reasoning": 326, "usage_raw": {"input_tokens": 459076, "cached_input_tokens": 416256, "output_tokens": 3141, "reasoning_output_tokens": 326}, "token_basis": "estimated", "tokens_proxy_input_uncached": 42820, "tokens_proxy_cache_read": 416256, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3141, "tokens_proxy_reasoning": 326, "tokens_proxy_calls": 14, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 45961, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_taskflow_53h08n29", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, "sha256": ""}, "tests/test_resources.py": {"bytes": 773, "mtime": 1784160991.2269812, "sha256": ""}, "tests/test_retry.py": {"bytes": 833, "mtime": 1784160991.2270508, "sha256": ""}, "tests/test_statemachine.py": {"bytes": 567, "mtime": 1784160991.2271175, "sha256": ""}, "taskflow/__init__.py": {"bytes": 2080, "mtime": 1784160991.2241614, "sha256": ""}, "taskflow/analysis.py": {"bytes": 8183, "mtime": 1784160991.224232, "sha256": ""}, "taskflow/builder.py": {"bytes": 8904, "mtime": 1784160991.2243223, "sha256": ""}, "taskflow/cli.py": {"bytes": 7365, "mtime": 1784160991.2243776, "sha256": ""}, "taskflow/config.py": {"bytes": 6988, "mtime": 1784430097.9359822, "sha256": ""}, "taskflow/dag.py": {"bytes": 15744, "mtime": 1784160991.224575, "sha256": ""}, "taskflow/dag_algorithms.py": {"bytes": 12614, "mtime": 1784160991.2246811, "sha256": ""}, "taskflow/diagnostics.py": {"bytes": 7380, "mtime": 1784160991.224753, "sha256": ""}, "taskflow/events.py": {"bytes": 6312, "mtime": 1784430098.3183043, "sha256": ""}, "taskflow/export.py": {"bytes": 5319, "mtime": 1784160991.2248626, "sha256": ""}, "taskflow/filters.py": {"bytes": 8531, "mtime": 1784160991.2249525, "sha256": ""}, "taskflow/history.py": {"bytes": 8566, "mtime": 1784160991.2250726, "sha256": ""}, "taskflow/hooks.py": {"bytes": 10205, "mtime": 1784160991.2251635, "sha256": ""}, "taskflow/metrics.py": {"bytes": 11622, "mtime": 1784160991.2252398, "sha256": ""}, "taskflow/model.py": {"bytes": 16049, "mtime": 1784160991.2253256, "sha256": ""}, "taskflow/planner.py": {"bytes": 11805, "mtime": 1784160991.225468, "sha256": ""}, "taskflow/policies.py": {"bytes": 10634, "mtime": 1784160991.2255614, "sha256": ""}, "taskflow/queue.py": {"bytes": 5687, "mtime": 1784430098.0143526, "sha256": ""}, "taskflow/reporting.py": {"bytes": 9271, "mtime": 1784160991.2257166, "sha256": ""}, "taskflow/resources.py": {"bytes": 7281, "mtime": 1784430098.2417123, "sha256": ""}, "taskflow/retry.py": {"bytes": 6056, "mtime": 1784430098.0903335, "sha256": ""}, "taskflow/runner.py": {"bytes": 4452, "mtime": 1784160991.2259035, "sha256": ""}, "taskflow/scheduler.py": {"bytes": 14635, "mtime": 1784430098.3936899, "sha256": ""}, "taskflow/serialization.py": {"bytes": 9103, "mtime": 1784160991.2260818, "sha256": ""}, "taskflow/simulation.py": {"bytes": 7494, "mtime": 1784160991.226148, "sha256": ""}, "taskflow/statemachine.py": {"bytes": 6500, "mtime": 1784430098.1648393, "sha256": ""}, "taskflow/stats.py": {"bytes": 12341, "mtime": 1784160991.226304, "sha256": ""}, "taskflow/templating.py": {"bytes": 8884, "mtime": 1784160991.2263978, "sha256": ""}, "taskflow/timeutil.py": {"bytes": 9180, "mtime": 1784160991.2264779, "sha256": ""}, "taskflow/validation.py": {"bytes": 12447, "mtime": 1784160991.2265797, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:taskflow:gpt-5.6-sol:trial2", "ts_iso": "2026-07-18T23:02:20", "harness": "codex", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 94.603, "t_env_setup_s": 0.007, "t_agent_s": 94.603, "t_checker_s": 0.235, "tokens": 45555, "tokens_input_uncached": 42393, "tokens_cache_read": 285184, "tokens_cache_write": null, "tokens_output": 3162, "tokens_reasoning": 437, "usage_raw": {"input_tokens": 327577, "cached_input_tokens": 285184, "output_tokens": 3162, "reasoning_output_tokens": 437}, "token_basis": "estimated", "tokens_proxy_input_uncached": 42393, "tokens_proxy_cache_read": 285184, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3162, "tokens_proxy_reasoning": 437, "tokens_proxy_calls": 10, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 45555, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_taskflow_1ca5n8qv", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, "sha256": ""}, "tests/test_resources.py": {"bytes": 773, "mtime": 1784160991.2269812, "sha256": ""}, "tests/test_retry.py": {"bytes": 833, "mtime": 1784160991.2270508, "sha256": ""}, "tests/test_statemachine.py": {"bytes": 567, "mtime": 1784160991.2271175, "sha256": ""}, "taskflow/__init__.py": {"bytes": 2080, "mtime": 1784160991.2241614, "sha256": ""}, "taskflow/analysis.py": {"bytes": 8183, "mtime": 1784160991.224232, "sha256": ""}, "taskflow/builder.py": {"bytes": 8904, "mtime": 1784160991.2243223, "sha256": ""}, "taskflow/cli.py": {"bytes": 7365, "mtime": 1784160991.2243776, "sha256": ""}, "taskflow/config.py": {"bytes": 6988, "mtime": 1784430214.3534074, "sha256": ""}, "taskflow/dag.py": {"bytes": 15744, "mtime": 1784160991.224575, "sha256": ""}, "taskflow/dag_algorithms.py": {"bytes": 12614, "mtime": 1784160991.2246811, "sha256": ""}, "taskflow/diagnostics.py": {"bytes": 7380, "mtime": 1784160991.224753, "sha256": ""}, "taskflow/events.py": {"bytes": 6312, "mtime": 1784430214.5762854, "sha256": ""}, "taskflow/export.py": {"bytes": 5319, "mtime": 1784160991.2248626, "sha256": ""}, "taskflow/filters.py": {"bytes": 8531, "mtime": 1784160991.2249525, "sha256": ""}, "taskflow/history.py": {"bytes": 8566, "mtime": 1784160991.2250726, "sha256": ""}, "taskflow/hooks.py": {"bytes": 10205, "mtime": 1784160991.2251635, "sha256": ""}, "taskflow/metrics.py": {"bytes": 11622, "mtime": 1784160991.2252398, "sha256": ""}, "taskflow/model.py": {"bytes": 16049, "mtime": 1784160991.2253256, "sha256": ""}, "taskflow/planner.py": {"bytes": 11805, "mtime": 1784160991.225468, "sha256": ""}, "taskflow/policies.py": {"bytes": 10634, "mtime": 1784160991.2255614, "sha256": ""}, "taskflow/queue.py": {"bytes": 5687, "mtime": 1784430214.5012627, "sha256": ""}, "taskflow/reporting.py": {"bytes": 9271, "mtime": 1784160991.2257166, "sha256": ""}, "taskflow/resources.py": {"bytes": 7281, "mtime": 1784430214.6508803, "sha256": ""}, "taskflow/retry.py": {"bytes": 6056, "mtime": 1784430214.4271634, "sha256": ""}, "taskflow/runner.py": {"bytes": 4452, "mtime": 1784160991.2259035, "sha256": ""}, "taskflow/scheduler.py": {"bytes": 14635, "mtime": 1784430214.8003473, "sha256": ""}, "taskflow/serialization.py": {"bytes": 9103, "mtime": 1784160991.2260818, "sha256": ""}, "taskflow/simulation.py": {"bytes": 7494, "mtime": 1784160991.226148, "sha256": ""}, "taskflow/statemachine.py": {"bytes": 6500, "mtime": 1784430214.7256935, "sha256": ""}, "taskflow/stats.py": {"bytes": 12341, "mtime": 1784160991.226304, "sha256": ""}, "taskflow/templating.py": {"bytes": 8884, "mtime": 1784160991.2263978, "sha256": ""}, "taskflow/timeutil.py": {"bytes": 9180, "mtime": 1784160991.2264779, "sha256": ""}, "taskflow/validation.py": {"bytes": 12447, "mtime": 1784160991.2265797, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "codex:taskflow:gpt-5.6-sol:trial3", "ts_iso": "2026-07-18T23:03:55", "harness": "codex", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 144.772, "t_env_setup_s": 0.007, "t_agent_s": 144.772, "t_checker_s": 0.234, "tokens": 56200, "tokens_input_uncached": 53020, "tokens_cache_read": 360448, "tokens_cache_write": null, "tokens_output": 3180, "tokens_reasoning": 437, "usage_raw": {"input_tokens": 413468, "cached_input_tokens": 360448, "output_tokens": 3180, "reasoning_output_tokens": 437}, "token_basis": "estimated", "tokens_proxy_input_uncached": 53020, "tokens_proxy_cache_read": 360448, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3180, "tokens_proxy_reasoning": 437, "tokens_proxy_calls": 12, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 56200, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_taskflow_igpvu7h0", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/xmhgUFxyxFb_tXO2qd1ULXsB/codex/backend-api/codex\"", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 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"/bench_codex_webcore_ohaw6c6k", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/9Rnt8bTfBETWbVOhPsJXqPq-/codex/backend-api/codex\"", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.3, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "rate_limited", "checker_stdout": "FAIL(feat) test_feature_converters.ConverterFeatureTest.test_int_converter_matches_digits_as_int\nFAIL(feat) test_feature_converters.ConverterFeatureTest.test_slug_converter_accepts_and_rejects\nFAIL(feat) test_feature_converters.ConverterFeatureTest.test_path_converter_matches_multiple_segments\nFAIL(feat) test_feature_fallthrough.FallthroughFeatureTest.test_int_route_wins_for_digits\nFAIL(feat) test_feature_fallthrough.FallthroughFeatureTest.test_non_int_falls_through_to_slug\nFAIL(feat) test_feature_fallthrough.FallthroughFeatureTest.test_no_candidate_is_404\nFAIL(feat) test_feature_precedence.PrecedenceFeatureTest.test_static_beats_dynamic_regardless_of_order\nFAIL(feat) test_feature_precedence.PrecedenceFeatureTest.test_path_converter_is_lowest_precedence\nFAIL(feat) test_feature_paramtypes.ParamTypeFeatureTest.test_int_param_arrives_as_int\nFAIL(feat) test_feature_paramtypes.ParamTypeFeatureTest.test_path_param_contains_slashes\nFAIL(feat) test_feature_mount.MountFeatureTest.test_parametric_prefix_and_route_params_merged\nFAIL(feat) test_feature_mount.MountFeatureTest.test_static_prefix_mount\nFAIL(feat) test_feature_mountmiddleware.MountMiddlewareFeatureTest.test_parent_then_child_then_handler\nFAIL(feat) test_feature_mountmiddleware.MountMiddlewareFeatureTest.test_parent_middleware_short_circuits_child\nFAIL(feat) test_feature_trailing.TrailingSlashFeatureTest.test_extra_slash_redirects_to_registered\nFAIL(feat) test_feature_trailing.TrailingSlashFeatureTest.test_query_string_preserved_on_redirect\nFAIL(feat) test_feature_trailing.TrailingSlashFeatureTest.test_registered_with_slash_redirects_missing_slash\nFAIL(feat) test_feature_method.MethodNotAllowedFeatureTest.test_wrong_method_is_405\nFAIL(feat) test_feature_method.MethodNotAllowedFeatureTest.test_allow_header_is_sorted\nFAIL(feat) test_feature_urlfor.UrlForFeatureTest.test_int_formatting_and_query_string\nFAIL(feat) test_feature_urlfor.UrlForFeatureTest.test_mount_prefix_included\nFAIL(feat) test_feature_urlfor.UrlForFeatureTest.test_missing_param_raises\nFAIL(feat) 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"proxy_measured", "tokens_fresh": null, "turns": null, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_webcore_5jq9sten", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:49817/cell/3dD_i_0Tw_066k4Riv6j_gyW/codex/backend-api/codex\"", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {".gitignore": {"bytes": 7, "mtime": 1784431817.1063087, "sha256": ""}, "README.md": {"bytes": 4651, "mtime": 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"gpt-5.6-sol", "task": "webcore", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 1769.921, "t_env_setup_s": 0.012, "t_agent_s": 1769.921, "t_checker_s": 0.244, "tokens": 169757, "tokens_input_uncached": 133721, "tokens_cache_read": 5199872, "tokens_cache_write": null, "tokens_output": 36036, "tokens_reasoning": 10179, "usage_raw": {"input_tokens": 5333593, "cached_input_tokens": 5199872, "output_tokens": 36036, "reasoning_output_tokens": 10179}, "token_basis": "estimated", "tokens_proxy_input_uncached": 684940, "tokens_proxy_cache_read": 8677632, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 99232, "tokens_proxy_reasoning": 41302, "tokens_proxy_calls": 223, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 169757, "turns": 1, "cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/bench_codex_webcore_u2zg6rzn", "-s", "workspace-write", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://127.0.0.1:-api/codex\"", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "codex-cli 0.144.5 (/opt/homebrew/bin/codex)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {".gitignore": {"bytes": 7, "mtime": 1784434100.2522006, "sha256": ""}, "README.md": {"bytes": 4929, "mtime": 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"codex", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 80.814, "t_env_setup_s": 0.183, "t_agent_s": 80.575, "t_checker_s": 0.228, "tokens": 11126, "tokens_input_uncached": 9313, "tokens_cache_read": 83200, "tokens_cache_write": null, "tokens_output": 1813, "tokens_reasoning": 489, "usage_raw": {"input_tokens": 92513, "cached_input_tokens": 83200, "output_tokens": 1813, "reasoning_output_tokens": 489}, "token_basis": "estimated", "tokens_proxy_input_uncached": 9313, "tokens_proxy_cache_read": 83200, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1813, "tokens_proxy_reasoning": 489, "tokens_proxy_calls": 7, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 11126, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_dfdd42ee", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_db-wal-recovery_zw4_r307:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_9t5mon35.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=kY2wyiDDnEDH7-Z6fVqwm3GY", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/kY2wyiDDnEDH7-Z6fVqwm3GY/codex/backend-api/codex\"", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784435728.6110737, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784435702.5468469, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784435712.3555448, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T00:35:33", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 50.563, "t_env_setup_s": 0.091, "t_agent_s": 50.417, "t_checker_s": 0.236, "tokens": 10832, "tokens_input_uncached": 9122, "tokens_cache_read": 83200, "tokens_cache_write": null, "tokens_output": 1710, "tokens_reasoning": 447, "usage_raw": {"input_tokens": 92322, "cached_input_tokens": 83200, "output_tokens": 1710, "reasoning_output_tokens": 447}, "token_basis": "estimated", "tokens_proxy_input_uncached": 9122, "tokens_proxy_cache_read": 83200, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1710, "tokens_proxy_reasoning": 447, "tokens_proxy_calls": 7, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 10832, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_e3f8c3e4", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_db-wal-recovery_yh7qf59m:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_5i9vsyc5.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=anEJKW0mHdZWafHYi9xOw8en", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784435781.3634539, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784435767.52411, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784435774.5097911, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T00:36:24", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 66.236, "t_env_setup_s": 0.089, "t_agent_s": 66.09, "t_checker_s": 0.235, "tokens": 11323, "tokens_input_uncached": 9381, "tokens_cache_read": 84224, "tokens_cache_write": null, "tokens_output": 1942, "tokens_reasoning": 406, "usage_raw": {"input_tokens": 93605, "cached_input_tokens": 84224, "output_tokens": 1942, "reasoning_output_tokens": 406}, "token_basis": "estimated", "tokens_proxy_input_uncached": 9381, "tokens_proxy_cache_read": 84224, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1942, "tokens_proxy_reasoning": 406, "tokens_proxy_calls": 7, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 11323, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_15584bd6", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_db-wal-recovery_3f8ihljo:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_velzpzta.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=JgDNEqFTkRBqDNebOeAknvge", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784435846.9340692, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784435832.2948737, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784435840.4455175, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/extract-elf:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T00:37:30", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 155.819, "t_env_setup_s": 0.089, "t_agent_s": 155.679, "t_checker_s": 0.293, "tokens": 34057, "tokens_input_uncached": 28934, "tokens_cache_read": 172032, "tokens_cache_write": null, "tokens_output": 5123, "tokens_reasoning": 1886, "usage_raw": {"input_tokens": 200966, "cached_input_tokens": 172032, "output_tokens": 5123, "reasoning_output_tokens": 1886}, "token_basis": "estimated", "tokens_proxy_input_uncached": 28934, "tokens_proxy_cache_read": 172032, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5123, "tokens_proxy_reasoning": 1886, "tokens_proxy_calls": 12, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 34057, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_9fb4ab2a", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_extract-elf_d5nmz3p0:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_djd527ev.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=tzqKRvhlXwpBk6jQJaGuCzn7", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3887, "mtime": 1784435965.8793323, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/extract-elf:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T00:40:07", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 186.645, "t_env_setup_s": 0.083, "t_agent_s": 186.516, "t_checker_s": 0.292, "tokens": 42516, "tokens_input_uncached": 37142, "tokens_cache_read": 189440, "tokens_cache_write": null, "tokens_output": 5374, "tokens_reasoning": 2073, "usage_raw": {"input_tokens": 226582, "cached_input_tokens": 189440, "output_tokens": 5374, "reasoning_output_tokens": 2073}, "token_basis": "estimated", "tokens_proxy_input_uncached": 37142, "tokens_proxy_cache_read": 189440, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5374, "tokens_proxy_reasoning": 2073, "tokens_proxy_calls": 13, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 42516, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_02eee6c9", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_extract-elf_955oh6x7:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_lszm5v0s.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=kIFRZjsVgNEqZ2dAQl7naMpV", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3186, "mtime": 1784436168.314259, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/extract-elf:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T00:43:14", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 196.344, "t_env_setup_s": 0.082, "t_agent_s": 196.202, "t_checker_s": 0.29, "tokens": 44314, "tokens_input_uncached": 38180, "tokens_cache_read": 344576, "tokens_cache_write": null, "tokens_output": 6134, "tokens_reasoning": 2324, "usage_raw": {"input_tokens": 382756, "cached_input_tokens": 344576, "output_tokens": 6134, "reasoning_output_tokens": 2324}, "token_basis": "estimated", "tokens_proxy_input_uncached": 38180, "tokens_proxy_cache_read": 344576, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6134, "tokens_proxy_reasoning": 2324, "tokens_proxy_calls": 14, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 44314, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_05bd1056", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_extract-elf_ecuwdsib:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_6qrg3z4a.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=0Ooatok8CSrrlDfaRqbtYEiA", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3335, "mtime": 1784436348.7957077, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T00:46:31", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 1, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 69.093, "t_env_setup_s": 0.081, "t_agent_s": 68.956, "t_checker_s": 0.237, "tokens": null, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 3208, "tokens_proxy_cache_read": 20992, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 734, "tokens_proxy_reasoning": 423, "tokens_proxy_calls": 3, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": null, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_81b7060e", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_feal-differential-cryptanalysis_d556qibx:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_2xya4bar.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=KYCuB6vNl5mUPZ8BNJuTH_RK", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/KYCuB6vNl5mUPZ8BNJuTH_RK/codex/backend-api/codex\"", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T00:47:40", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 2, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 69.544, "t_env_setup_s": 0.086, "t_agent_s": 69.402, "t_checker_s": 0.238, "tokens": null, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 1729, "tokens_proxy_cache_read": 9984, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 109, "tokens_proxy_reasoning": 0, "tokens_proxy_calls": 2, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": null, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_b0e03b67", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_feal-differential-cryptanalysis_yklrxhuc:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_030euqf2.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=n2sg4u6fNJmGyAA71jv-A8ga", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/n2sg4u6fNJmGyAA71jv-A8ga/codex/backend-api/codex\"", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T00:48:50", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 3, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 56.715, "t_env_setup_s": 0.088, "t_agent_s": 56.575, "t_checker_s": 0.238, "tokens": null, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 10387, "tokens_proxy_cache_read": 13824, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 819, "tokens_proxy_reasoning": 473, "tokens_proxy_calls": 3, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": null, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_60c3b126", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_feal-differential-cryptanalysis_eqiu7vtw:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_qlgn95bw.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=DuFbe9PV6WK4JBuCt_ZnTdhc", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/DuFbe9PV6WK4JBuCt_ZnTdhc/codex/backend-api/codex\"", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/gcode-to-text:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T00:49:47", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 224.689, "t_env_setup_s": 0.085, "t_agent_s": 224.553, "t_checker_s": 0.236, "tokens": 55301, "tokens_input_uncached": 48853, "tokens_cache_read": 386304, "tokens_cache_write": null, "tokens_output": 6448, "tokens_reasoning": 1914, "usage_raw": {"input_tokens": 435157, "cached_input_tokens": 386304, "output_tokens": 6448, "reasoning_output_tokens": 1914}, "token_basis": "estimated", "tokens_proxy_input_uncached": 48853, "tokens_proxy_cache_read": 386304, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6448, "tokens_proxy_reasoning": 1914, "tokens_proxy_calls": 24, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 55301, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_7ff3614d", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_gcode-to-text_zx3yacij:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_phhesrwv.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=BPRMk1c_jvKVywnSopr3CL-k", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/BPRMk1c_jvKVywnSopr3CL-k/codex/backend-api/codex\"", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: out.txt does not contain the decoded printed text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784436787.5058792, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/gcode-to-text:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T00:53:32", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 273.289, "t_env_setup_s": 0.086, "t_agent_s": 273.151, "t_checker_s": 0.235, "tokens": 73632, "tokens_input_uncached": 65886, "tokens_cache_read": 536576, "tokens_cache_write": null, "tokens_output": 7746, "tokens_reasoning": 1914, "usage_raw": {"input_tokens": 602462, "cached_input_tokens": 536576, "output_tokens": 7746, "reasoning_output_tokens": 1914}, "token_basis": "estimated", "tokens_proxy_input_uncached": 65886, "tokens_proxy_cache_read": 536576, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 7746, "tokens_proxy_reasoning": 1914, "tokens_proxy_calls": 25, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 73632, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_038bcf59", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_gcode-to-text_6_gwml57:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_g0xqgyrb.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=ZjP9x1PuFYvN7vRgqfyI3_KM", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/ZjP9x1PuFYvN7vRgqfyI3_KM/codex/backend-api/codex\"", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: out.txt does not contain the decoded printed text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 28, "mtime": 1784437077.046066, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/gcode-to-text:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T00:58:06", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 208.689, "t_env_setup_s": 0.09, "t_agent_s": 208.533, "t_checker_s": 0.238, "tokens": 46361, "tokens_input_uncached": 39150, "tokens_cache_read": 305920, "tokens_cache_write": null, "tokens_output": 7211, "tokens_reasoning": 1895, "usage_raw": {"input_tokens": 345070, "cached_input_tokens": 305920, "output_tokens": 7211, "reasoning_output_tokens": 1895}, "token_basis": "estimated", "tokens_proxy_input_uncached": 39150, "tokens_proxy_cache_read": 305920, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 7211, "tokens_proxy_reasoning": 1895, "tokens_proxy_calls": 17, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 46361, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_cb5b3644", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_gcode-to-text_ar6yh9kp:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_jdf2vlto.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=Hy54mftRZXln7QEqQSdNW65z", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784437282.9025695, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T01:01:35", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 224.395, "t_env_setup_s": 0.089, "t_agent_s": 224.268, "t_checker_s": 0.226, "tokens": 46444, "tokens_input_uncached": 39593, "tokens_cache_read": 279296, "tokens_cache_write": null, "tokens_output": 6851, "tokens_reasoning": 2183, "usage_raw": {"input_tokens": 318889, "cached_input_tokens": 279296, "output_tokens": 6851, "reasoning_output_tokens": 2183}, "token_basis": "estimated", "tokens_proxy_input_uncached": 39593, "tokens_proxy_cache_read": 279296, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6851, "tokens_proxy_reasoning": 2183, "tokens_proxy_calls": 9, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 46444, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_8a55779b", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_llm-inference-batching-scheduler_q6czfouj:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_gx0w2p62.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=-z-m2xN_pImLP6Pql7JPoE6w", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/-z-m2xN_pImLP6Pql7JPoE6w/codex/backend-api/codex\"", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/shape_aware_packer.py": {"bytes": 5135, "mtime": 1784437497.083532, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88800, "mtime": 1784437511.673061, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 90400, "mtime": 1784437511.6803632, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T01:05:20", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 213.941, "t_env_setup_s": 0.087, "t_agent_s": 213.801, "t_checker_s": 0.233, "tokens": 49453, "tokens_input_uncached": 41208, "tokens_cache_read": 200960, "tokens_cache_write": null, "tokens_output": 8245, "tokens_reasoning": 2924, "usage_raw": {"input_tokens": 242168, "cached_input_tokens": 200960, "output_tokens": 8245, "reasoning_output_tokens": 2924}, "token_basis": "estimated", "tokens_proxy_input_uncached": 41208, "tokens_proxy_cache_read": 200960, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 8245, "tokens_proxy_reasoning": 2924, "tokens_proxy_calls": 11, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 49453, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_37e39265", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_llm-inference-batching-scheduler_23afyjgy:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_hzm6ev7t.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=MeSu-CLly6nCnt6Jr6qi3SEN", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/MeSu-CLly6nCnt6Jr6qi3SEN/codex/backend-api/codex\"", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/shape_aware_packer.py": {"bytes": 2305, "mtime": 1784437701.4439182, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784437717.5172193, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784437717.5193064, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T01:08:54", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 177.798, "t_env_setup_s": 0.085, "t_agent_s": 177.666, "t_checker_s": 0.238, "tokens": 24946, "tokens_input_uncached": 19388, "tokens_cache_read": 138240, "tokens_cache_write": null, "tokens_output": 5558, "tokens_reasoning": 2044, "usage_raw": {"input_tokens": 157628, "cached_input_tokens": 138240, "output_tokens": 5558, "reasoning_output_tokens": 2044}, "token_basis": "estimated", "tokens_proxy_input_uncached": 19388, "tokens_proxy_cache_read": 138240, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5558, "tokens_proxy_reasoning": 2044, "tokens_proxy_calls": 8, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 24946, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_ac8ecdcb", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_llm-inference-batching-scheduler_5ituv7_k:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_hdong3ud.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=ywTprAf5GBqs4f2kM_YccUJj", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/ywTprAf5GBqs4f2kM_YccUJj/codex/backend-api/codex\"", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/shape_aware_packer.py": {"bytes": 2490, "mtime": 1784437872.5368946, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88800, "mtime": 1784437878.1798332, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784437878.184059, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/raman-fitting:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T01:11:52", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 226.235, "t_env_setup_s": 0.087, "t_agent_s": 226.1, "t_checker_s": 0.224, "tokens": 43803, "tokens_input_uncached": 35571, "tokens_cache_read": 215296, "tokens_cache_write": null, "tokens_output": 8232, "tokens_reasoning": 3014, "usage_raw": {"input_tokens": 250867, "cached_input_tokens": 215296, "output_tokens": 8232, "reasoning_output_tokens": 3014}, "token_basis": "estimated", "tokens_proxy_input_uncached": 35571, "tokens_proxy_cache_read": 215296, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 8232, "tokens_proxy_reasoning": 3014, "tokens_proxy_calls": 15, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 43803, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_dbab0de5", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_raman-fitting_e42gc4q0:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_opgss1cm.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=56qBau1XbooAJeePtKecWZAs", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: 2D parameters outside tolerance: offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 237, "mtime": 1784438132.347935, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/raman-fitting:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T01:15:39", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 213.395, "t_env_setup_s": 0.087, "t_agent_s": 213.252, "t_checker_s": 0.238, "tokens": 45344, "tokens_input_uncached": 39607, "tokens_cache_read": 177920, "tokens_cache_write": null, "tokens_output": 5737, "tokens_reasoning": 2622, "usage_raw": {"input_tokens": 217527, "cached_input_tokens": 177920, "output_tokens": 5737, "reasoning_output_tokens": 2622}, "token_basis": "estimated", "tokens_proxy_input_uncached": 39607, "tokens_proxy_cache_read": 177920, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5737, "tokens_proxy_reasoning": 2622, "tokens_proxy_calls": 14, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 45344, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_6cc25b8b", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_raman-fitting_nry4tll9:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_2yb45cyl.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=MX6c6gRoKeMadz8puhTzzkQ8", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 227, "mtime": 1784438340.2714622, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/raman-fitting:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T01:19:13", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 221.496, "t_env_setup_s": 0.087, "t_agent_s": 221.355, "t_checker_s": 0.241, "tokens": 36219, "tokens_input_uncached": 28377, "tokens_cache_read": 226816, "tokens_cache_write": null, "tokens_output": 7842, "tokens_reasoning": 3692, "usage_raw": {"input_tokens": 255193, "cached_input_tokens": 226816, "output_tokens": 7842, "reasoning_output_tokens": 3692}, "token_basis": "estimated", "tokens_proxy_input_uncached": 28377, "tokens_proxy_cache_read": 226816, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 7842, "tokens_proxy_reasoning": 3692, "tokens_proxy_calls": 14, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 36219, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_33063e86", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_raman-fitting_mihczcdl:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_87hhpuvk.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=UqbsGiYIVspl9fTY4svStQAb", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 239, "mtime": 1784438565.837469, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T01:22:55", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 749.033, "t_env_setup_s": 0.096, "t_agent_s": 748.894, "t_checker_s": 27.173, "tokens": 72163, "tokens_input_uncached": 57158, "tokens_cache_read": 1240320, "tokens_cache_write": null, "tokens_output": 15005, "tokens_reasoning": 6592, "usage_raw": {"input_tokens": 1297478, "cached_input_tokens": 1240320, "output_tokens": 15005, "reasoning_output_tokens": 6592}, "token_basis": "estimated", "tokens_proxy_input_uncached": 57158, "tokens_proxy_cache_read": 1240320, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 15005, "tokens_proxy_reasoning": 6592, "tokens_proxy_calls": 35, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 72163, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_a3736508", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_schemelike-metacircular-eval_fbktj7df:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_6mxfbtt6.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=FXohBd21Vixzkn9cqhgKrU4W", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:-api/codex\"", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784439233.5142639, "sha256": ""}, "eval.scm": {"bytes": 10491, "mtime": 1784439308.473184, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, "sha256": ""}, "test/06-interactive-io.scm": {"bytes": 992, "mtime": 1784160991.2214541, "sha256": ""}, "test/08-progn-sequencing.scm": {"bytes": 1067, "mtime": 1784160991.2214954, "sha256": ""}, "test/09-mutual-recursion.scm": {"bytes": 895, "mtime": 1784160991.2215376, "sha256": ""}, "test/10-advanced-features.scm": {"bytes": 1945, "mtime": 1784160991.2215784, "sha256": ""}, "test/README.md": {"bytes": 2108, "mtime": 1784160991.2216206, "sha256": ""}, "test/accumulator_patterns.scm": {"bytes": 1410, "mtime": 1784160991.2216601, "sha256": ""}, "test/binary_tree.scm": {"bytes": 1311, "mtime": 1784160991.2217, "sha256": ""}, "test/calculator.scm": {"bytes": 341, "mtime": 1784160991.2217395, "sha256": ""}, "test/church_numerals.scm": {"bytes": 1179, "mtime": 1784160991.2217798, "sha256": ""}, "test/closures.scm": {"bytes": 904, "mtime": 1784160991.221818, "sha256": ""}, "test/continuation_passing.scm": {"bytes": 1104, "mtime": 1784160991.2218595, "sha256": ""}, "test/currying.scm": {"bytes": 1578, "mtime": 1784160991.2218997, "sha256": ""}, "test/filter_operations.scm": {"bytes": 719, "mtime": 1784160991.2219415, "sha256": ""}, "test/fold_operations.scm": {"bytes": 800, "mtime": 1784160991.2219827, "sha256": ""}, "test/function_composition.scm": {"bytes": 1397, "mtime": 1784160991.2220268, "sha256": ""}, "test/lazy_evaluation.scm": {"bytes": 1279, "mtime": 1784160991.2220654, "sha256": ""}, "test/list_operations.scm": {"bytes": 1683, "mtime": 1784160991.222102, "sha256": ""}, "test/map_operations.scm": {"bytes": 637, "mtime": 1784160991.222143, "sha256": ""}, "test/memoization.scm": {"bytes": 1694, "mtime": 1784160991.2221804, "sha256": ""}, "test/mutual_recursion.scm": {"bytes": 1892, "mtime": 1784160991.222222, "sha256": ""}, "test/nested_defines.scm": {"bytes": 1133, "mtime": 1784160991.2222624, "sha256": ""}, "test/oeis_sequences.scm": {"bytes": 2009, "mtime": 1784160991.2223027, "sha256": ""}, "test/oeis_sequences2.scm": {"bytes": 2351, "mtime": 1784160991.222343, "sha256": ""}, "test/oeis_sequences3.scm": {"bytes": 2555, "mtime": 1784160991.2223866, "sha256": ""}, "test/recursive_structures.scm": {"bytes": 2780, "mtime": 1784160991.2224295, "sha256": ""}, "test/test_read.scm": {"bytes": 84, "mtime": 1784160991.222468, "sha256": ""}, "test/variadic_functions.scm": {"bytes": 2468, "mtime": 1784160991.2225044, "sha256": ""}, "test/y_combinator.scm": {"bytes": 846, "mtime": 1784160991.2225444, "sha256": ""}, "shadow_test/01-factorial.scm": {"bytes": 548, "mtime": 1784160991.2199306, "sha256": ""}, "shadow_test/02-fibonacci.scm": {"bytes": 811, "mtime": 1784160991.2199712, "sha256": ""}, "shadow_test/03-list-operations.scm": {"bytes": 1198, "mtime": 1784160991.2200122, "sha256": ""}, "shadow_test/04-higher-order.scm": {"bytes": 988, "mtime": 1784160991.2200537, "sha256": ""}, "shadow_test/05-simple-io.scm": {"bytes": 947, "mtime": 1784160991.2200944, "sha256": ""}, "shadow_test/06-interactive-io.scm": {"bytes": 1032, "mtime": 1784160991.220135, "sha256": ""}, "shadow_test/08-progn-sequencing.scm": {"bytes": 1087, "mtime": 1784160991.220178, "sha256": ""}, "shadow_test/09-mutual-recursion.scm": {"bytes": 986, "mtime": 1784160991.2202172, "sha256": ""}, "shadow_test/10-advanced-features.scm": {"bytes": 2052, "mtime": 1784160991.220261, "sha256": ""}, "shadow_test/accumulator_patterns.scm": {"bytes": 1427, "mtime": 1784160991.220304, "sha256": ""}, "shadow_test/binary_tree.scm": {"bytes": 1342, "mtime": 1784160991.2203417, "sha256": ""}, "shadow_test/church_numerals.scm": {"bytes": 1225, "mtime": 1784160991.220378, "sha256": ""}, "shadow_test/closures.scm": {"bytes": 911, "mtime": 1784160991.2204163, "sha256": ""}, "shadow_test/continuation_passing.scm": {"bytes": 1133, "mtime": 1784160991.2204528, "sha256": ""}, "shadow_test/currying.scm": {"bytes": 1610, "mtime": 1784160991.2204933, "sha256": ""}, "shadow_test/filter_operations.scm": {"bytes": 749, "mtime": 1784160991.2205327, "sha256": ""}, "shadow_test/fold_operations.scm": {"bytes": 821, "mtime": 1784160991.2206206, "sha256": ""}, "shadow_test/function_composition.scm": {"bytes": 1194, "mtime": 1784160991.2206597, "sha256": ""}, "shadow_test/lazy_evaluation.scm": {"bytes": 1294, "mtime": 1784160991.2206986, "sha256": ""}, "shadow_test/list_operations.scm": {"bytes": 1680, "mtime": 1784160991.220741, "sha256": ""}, "shadow_test/map_operations.scm": {"bytes": 651, "mtime": 1784160991.2207797, "sha256": ""}, "shadow_test/memoization.scm": {"bytes": 1644, "mtime": 1784160991.2208223, "sha256": ""}, "shadow_test/mutual_recursion.scm": {"bytes": 1913, "mtime": 1784160991.220859, "sha256": ""}, "shadow_test/nested_defines.scm": {"bytes": 1154, "mtime": 1784160991.2209008, "sha256": ""}, "shadow_test/oeis_sequences.scm": {"bytes": 1943, "mtime": 1784160991.2209404, "sha256": ""}, "shadow_test/oeis_sequences2.scm": {"bytes": 2364, "mtime": 1784160991.2209766, "sha256": ""}, "shadow_test/oeis_sequences3.scm": {"bytes": 2565, "mtime": 1784160991.2210133, "sha256": ""}, "shadow_test/recursive_structures.scm": {"bytes": 2994, "mtime": 1784160991.221053, "sha256": ""}, "shadow_test/test_read.scm": {"bytes": 90, "mtime": 1784160991.22109, "sha256": ""}, "shadow_test/variadic_functions.scm": {"bytes": 2434, "mtime": 1784160991.221138, "sha256": ""}, "shadow_test/y_combinator.scm": {"bytes": 877, "mtime": 1784160991.2211754, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "codex:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T01:35:51", "harness": "codex", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 2, "success": false, "completed": false, "error": "timeout after 2400s", "wall_time_s": 2400.387, "t_env_setup_s": 0.091, "t_agent_s": 2400.261, "t_checker_s": 0.288, "tokens": null, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 148933, "tokens_proxy_cache_read": 5690624, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 38259, "tokens_proxy_reasoning": 17056, "tokens_proxy_calls": 112, "sampling_observed": [{"model": "gpt-5.6-sol", "reasoning_effort": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": null, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_codex_93359_a353347e", "-v", "/dev/openbench/.bench-tmp/bench_codex_terminal-bench_schemelike-metacircular-eval_a8w2rccm:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ahl6xc9r.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY=openbench-bridge-placeholder", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=QHwVuu478cSPDszz_vLKCk0g", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/QHwVuu478cSPDszz_vLKCk0g/codex/backend-api/codex\"", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "timeout", "checker_stdout": "FAIL: /tasks-imported/terminal-bench/schemelike-metacircular-eval/checker_data/shadow_test/01-factorial.scm: eval.scm output != interp.py output\n direct: '720\\n40320\\n1307674368000'\n via eval: \"[RECURSION] Deep recursion detected! Last expression: 'null?'\\nUnexpected error: Recursion limit exceeded\"\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784441155.4176817, "sha256": ""}, "empty.scm": {"bytes": 9, "mtime": 1784441456.112867, "sha256": ""}, "eval.scm": {"bytes": 16282, "mtime": 1784441710.0313892, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, "sha256": ""}, "test/06-interactive-io.scm": {"bytes": 992, "mtime": 1784160991.2214541, "sha256": ""}, "test/08-progn-sequencing.scm": {"bytes": 1067, "mtime": 1784160991.2214954, "sha256": ""}, "test/09-mutual-recursion.scm": {"bytes": 895, "mtime": 1784160991.2215376, "sha256": ""}, "test/10-advanced-features.scm": {"bytes": 1945, "mtime": 1784160991.2215784, "sha256": ""}, "test/README.md": {"bytes": 2108, "mtime": 1784160991.2216206, "sha256": ""}, "test/accumulator_patterns.scm": {"bytes": 1410, "mtime": 1784160991.2216601, "sha256": ""}, "test/binary_tree.scm": {"bytes": 1311, "mtime": 1784160991.2217, "sha256": ""}, "test/calculator.scm": {"bytes": 341, "mtime": 1784160991.2217395, "sha256": ""}, "test/church_numerals.scm": {"bytes": 1179, "mtime": 1784160991.2217798, "sha256": ""}, "test/closures.scm": {"bytes": 904, "mtime": 1784160991.221818, "sha256": ""}, "test/continuation_passing.scm": {"bytes": 1104, "mtime": 1784160991.2218595, "sha256": ""}, "test/currying.scm": {"bytes": 1578, "mtime": 1784160991.2218997, "sha256": ""}, "test/filter_operations.scm": {"bytes": 719, "mtime": 1784160991.2219415, "sha256": ""}, "test/fold_operations.scm": {"bytes": 800, "mtime": 1784160991.2219827, "sha256": ""}, "test/function_composition.scm": {"bytes": 1397, "mtime": 1784160991.2220268, "sha256": ""}, "test/lazy_evaluation.scm": {"bytes": 1279, "mtime": 1784160991.2220654, "sha256": ""}, "test/list_operations.scm": {"bytes": 1683, "mtime": 1784160991.222102, "sha256": ""}, "test/map_operations.scm": {"bytes": 637, "mtime": 1784160991.222143, "sha256": ""}, "test/memoization.scm": {"bytes": 1694, "mtime": 1784160991.2221804, "sha256": ""}, "test/mutual_recursion.scm": {"bytes": 1892, "mtime": 1784160991.222222, "sha256": ""}, "test/nested_defines.scm": {"bytes": 1133, "mtime": 1784160991.2222624, "sha256": ""}, "test/oeis_sequences.scm": {"bytes": 2009, "mtime": 1784160991.2223027, "sha256": ""}, "test/oeis_sequences2.scm": {"bytes": 2351, "mtime": 1784160991.222343, "sha256": ""}, "test/oeis_sequences3.scm": {"bytes": 2555, "mtime": 1784160991.2223866, "sha256": ""}, "test/recursive_structures.scm": 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"OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:54447", "-e", "OPENBENCH_PROXY_CELL_TOKEN=e-wQnOzOpjznX_N-Ieklnv8b", "-v", "/.codex/auth.json:/bench/auth/.codex/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "codex", "gpt-5.6-sol", "2400"], "adapter_cmd": ["codex", "exec", "--json", "--disable", "apps", "--disable", "plugins", "--disable", "multi_agent", "--skip-git-repo-check", "-C", "/work", "--dangerously-bypass-approvals-and-sandbox", "-m", "gpt-5.6-sol", "-c", "model_reasoning_effort=\"medium\"", "-c", "service_tier=\"default\"", "-c", "openai_base_url=\"http://host.docker.internal:54447/cell/e-wQnOzOpjznX_N-Ieklnv8b/codex/backend-api/codex\"", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784442179.4429362, "sha256": ""}, "eval.scm": {"bytes": 9771, "mtime": 1784442133.1187103, "sha256": ""}, "handler-test.txt": {"bytes": 23, "mtime": 1784442069.1693275, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, 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"tokens_proxy_output": 2630, "tokens_proxy_reasoning": 895, "tokens_proxy_calls": 8, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 19080, "turns": 8, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 755, "mtime": 1783083001.4625738, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1620, "mtime": 1784442293.7722373, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:add-feature:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T02:25:30", "harness": "pi", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 72.177, "t_env_setup_s": 0.003, "t_agent_s": 72.177, "t_checker_s": 0.248, "tokens": 16117, "tokens_input_uncached": 13704, "tokens_cache_read": 12288, "tokens_cache_write": 0, "tokens_output": 2413, "tokens_reasoning": 1063, "usage_raw": [{"input": 1556, "output": 112, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1668, "cost": {"input": 0.0077800000000000005, "output": 0.00336, "cacheRead": 0, "cacheWrite": 0, "total": 0.01114}}, {"input": 1920, "output": 135, "cacheRead": 0, "cacheWrite": 0, "reasoning": 14, "totalTokens": 2055, "cost": {"input": 0.009600000000000001, "output": 0.00405, "cacheRead": 0, "cacheWrite": 0, "total": 0.01365}}, {"input": 3029, "output": 1398, "cacheRead": 0, "cacheWrite": 0, "reasoning": 916, "totalTokens": 4427, "cost": {"input": 0.015145, "output": 0.04194, "cacheRead": 0, "cacheWrite": 0, "total": 0.057085}}, {"input": 1892, "output": 126, "cacheRead": 2560, "cacheWrite": 0, "reasoning": 20, "totalTokens": 4578, "cost": {"input": 0.009460000000000001, "output": 0.00378, "cacheRead": 0.0012799999999999999, "cacheWrite": 0, "total": 0.014520000000000002}}, {"input": 1020, "output": 116, "cacheRead": 3584, "cacheWrite": 0, "reasoning": 8, "totalTokens": 4720, "cost": {"input": 0.0051, "output": 0.00348, "cacheRead": 0.001792, "cacheWrite": 0, "total": 0.010372000000000001}}, {"input": 3456, "output": 432, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 97, "totalTokens": 5424, "cost": {"input": 0.01728, "output": 0.012960000000000001, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.031008000000000004}}, {"input": 831, "output": 94, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5533, "cost": {"input": 0.004155000000000001, "output": 0.00282, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.009279}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 13704, "tokens_proxy_cache_read": 12288, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2413, "tokens_proxy_reasoning": 1063, "tokens_proxy_calls": 7, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 16117, "turns": 7, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 755, "mtime": 1783083001.4625738, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1472, "mtime": 1784442372.608439, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:add-feature:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T02:26:42", "harness": "pi", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 70.226, "t_env_setup_s": 0.002, "t_agent_s": 70.226, "t_checker_s": 0.249, "tokens": 18877, "tokens_input_uncached": 15643, "tokens_cache_read": 15872, "tokens_cache_write": 0, "tokens_output": 3234, "tokens_reasoning": 1237, "usage_raw": [{"input": 1558, "output": 96, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1654, "cost": {"input": 0.007790000000000001, "output": 0.00288, "cacheRead": 0, "cacheWrite": 0, "total": 0.01067}}, {"input": 1877, "output": 113, "cacheRead": 0, "cacheWrite": 0, "reasoning": 11, "totalTokens": 1990, "cost": {"input": 0.009385000000000001, "output": 0.0033900000000000002, "cacheRead": 0, "cacheWrite": 0, "total": 0.012775000000000002}}, {"input": 1363, "output": 1077, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 453, "totalTokens": 3976, "cost": {"input": 0.006815000000000001, "output": 0.03231, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.039893}}, {"input": 1441, "output": 425, "cacheRead": 2560, "cacheWrite": 0, "reasoning": 69, "totalTokens": 4426, "cost": {"input": 0.0072050000000000005, "output": 0.012750000000000001, "cacheRead": 0.0012799999999999999, "cacheWrite": 0, "total": 0.021235}}, {"input": 867, "output": 380, "cacheRead": 3584, "cacheWrite": 0, "reasoning": 24, "totalTokens": 4831, "cost": {"input": 0.004335, "output": 0.0114, "cacheRead": 0.001792, "cacheWrite": 0, "total": 0.017526999999999997}}, {"input": 1297, "output": 371, "cacheRead": 3584, "cacheWrite": 0, "reasoning": 8, "totalTokens": 5252, "cost": {"input": 0.006485, "output": 0.011130000000000001, "cacheRead": 0.001792, "cacheWrite": 0, "total": 0.019407}}, {"input": 5621, "output": 227, "cacheRead": 0, "cacheWrite": 0, "reasoning": 203, "totalTokens": 5848, "cost": {"input": 0.028105, "output": 0.00681, "cacheRead": 0, "cacheWrite": 0, "total": 0.034915}}, {"input": 1619, "output": 545, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 461, "totalTokens": 6772, "cost": {"input": 0.008095000000000001, "output": 0.01635, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.026749000000000002}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 15643, "tokens_proxy_cache_read": 15872, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3234, "tokens_proxy_reasoning": 1237, "tokens_proxy_calls": 8, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 18877, "turns": 8, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 755, "mtime": 1783083001.4625738, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1792, "mtime": 1784442439.517229, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:build-a-cli:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T02:27:53", "harness": "pi", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 17.953, "t_env_setup_s": 0.001, "t_agent_s": 17.953, "t_checker_s": 0.297, "tokens": 8057, "tokens_input_uncached": 7350, "tokens_cache_read": 0, "tokens_cache_write": 0, "tokens_output": 707, "tokens_reasoning": 108, "usage_raw": [{"input": 1394, "output": 81, "cacheRead": 0, "cacheWrite": 0, "reasoning": 16, "totalTokens": 1475, "cost": {"input": 0.0069700000000000005, "output": 0.00243, "cacheRead": 0, "cacheWrite": 0, "total": 0.0094}}, {"input": 1595, "output": 459, "cacheRead": 0, "cacheWrite": 0, "reasoning": 92, "totalTokens": 2054, "cost": {"input": 0.007975000000000001, "output": 0.013770000000000001, "cacheRead": 0, "cacheWrite": 0, "total": 0.021745}}, {"input": 2074, "output": 144, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 2218, "cost": {"input": 0.01037, "output": 0.00432, "cacheRead": 0, "cacheWrite": 0, "total": 0.014690000000000002}}, {"input": 2287, "output": 23, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 2310, "cost": {"input": 0.011435, "output": 0.00069, "cacheRead": 0, "cacheWrite": 0, "total": 0.012125}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 7350, "tokens_proxy_cache_read": 0, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 707, "tokens_proxy_reasoning": 108, "tokens_proxy_calls": 4, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8057, "turns": 4, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1195, "mtime": 1784442486.0232255, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:build-a-cli:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T02:28:11", "harness": "pi", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 20.923, "t_env_setup_s": 0.001, "t_agent_s": 20.923, "t_checker_s": 0.3, "tokens": 8026, "tokens_input_uncached": 7308, "tokens_cache_read": 0, "tokens_cache_write": 0, "tokens_output": 718, "tokens_reasoning": 152, "usage_raw": [{"input": 1394, "output": 78, "cacheRead": 0, "cacheWrite": 0, "reasoning": 15, "totalTokens": 1472, "cost": {"input": 0.0069700000000000005, "output": 0.00234, "cacheRead": 0, "cacheWrite": 0, "total": 0.00931}}, {"input": 1579, "output": 470, "cacheRead": 0, "cacheWrite": 0, "reasoning": 127, "totalTokens": 2049, "cost": {"input": 0.007895000000000001, "output": 0.0141, "cacheRead": 0, "cacheWrite": 0, "total": 0.021995}}, {"input": 2069, "output": 131, "cacheRead": 0, "cacheWrite": 0, "reasoning": 10, "totalTokens": 2200, "cost": {"input": 0.010345, "output": 0.00393, "cacheRead": 0, "cacheWrite": 0, "total": 0.014275}}, {"input": 2266, "output": 39, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 2305, "cost": {"input": 0.011330000000000002, "output": 0.00117, "cacheRead": 0, "cacheWrite": 0, "total": 0.0125}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 7308, "tokens_proxy_cache_read": 0, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 718, "tokens_proxy_reasoning": 152, "tokens_proxy_calls": 4, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8026, "turns": 4, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1114, "mtime": 1784442505.2440755, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:build-a-cli:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T02:28:33", "harness": "pi", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 24.291, "t_env_setup_s": 0.0, "t_agent_s": 24.291, "t_checker_s": 0.293, "tokens": 5092, "tokens_input_uncached": 4364, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 728, "tokens_reasoning": 166, "usage_raw": [{"input": 1395, "output": 132, "cacheRead": 0, "cacheWrite": 0, "reasoning": 80, "totalTokens": 1527, "cost": {"input": 0.006975, "output": 0.00396, "cacheRead": 0, "cacheWrite": 0, "total": 0.010935}}, {"input": 1643, "output": 430, "cacheRead": 0, "cacheWrite": 0, "reasoning": 86, "totalTokens": 2073, "cost": {"input": 0.008215, "output": 0.0129, "cacheRead": 0, "cacheWrite": 0, "total": 0.021115000000000002}}, {"input": 557, "output": 143, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 2236, "cost": {"input": 0.002785, "output": 0.00429, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.007843000000000001}}, {"input": 769, "output": 23, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 2328, "cost": {"input": 0.0038450000000000003, "output": 0.00069, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.005303}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 4364, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 728, "tokens_proxy_reasoning": 166, "tokens_proxy_calls": 4, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 5092, "turns": 4, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1123, "mtime": 1784442531.4408772, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:fix-failing-test:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T02:28:57", "harness": "pi", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 10.541, "t_env_setup_s": 0.002, "t_agent_s": 10.541, "t_checker_s": 0.243, "tokens": 4861, "tokens_input_uncached": 4646, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 215, "tokens_reasoning": 17, "usage_raw": [{"input": 1171, "output": 83, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1254, "cost": {"input": 0.005855000000000001, "output": 0.00249, "cacheRead": 0, "cacheWrite": 0, "total": 0.008345000000000002}}, {"input": 1594, "output": 67, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1661, "cost": {"input": 0.007970000000000001, "output": 0.00201, "cacheRead": 0, "cacheWrite": 0, "total": 0.009980000000000001}}, {"input": 1682, "output": 26, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1708, "cost": {"input": 0.00841, "output": 0.00078, "cacheRead": 0, "cacheWrite": 0, "total": 0.00919}}, {"input": 199, "output": 39, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1774, "cost": {"input": 0.000995, "output": 0.00117, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.0029330000000000003}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 4646, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 215, "tokens_proxy_reasoning": 17, "tokens_proxy_calls": 4, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 4861, "turns": 4, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784442543.7654731, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:fix-failing-test:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T02:29:08", "harness": "pi", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 15.122, "t_env_setup_s": 0.001, "t_agent_s": 15.122, "t_checker_s": 0.241, "tokens": 4863, "tokens_input_uncached": 4646, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 217, "tokens_reasoning": 17, "usage_raw": [{"input": 1171, "output": 83, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1254, "cost": {"input": 0.005855000000000001, "output": 0.00249, "cacheRead": 0, "cacheWrite": 0, "total": 0.008345000000000002}}, {"input": 1594, "output": 67, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1661, "cost": {"input": 0.007970000000000001, "output": 0.00201, "cacheRead": 0, "cacheWrite": 0, "total": 0.009980000000000001}}, {"input": 1682, "output": 26, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1708, "cost": {"input": 0.00841, "output": 0.00078, "cacheRead": 0, "cacheWrite": 0, "total": 0.00919}}, {"input": 199, "output": 41, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1776, "cost": {"input": 0.000995, "output": 0.00123, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.002993}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 4646, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 217, "tokens_proxy_reasoning": 17, "tokens_proxy_calls": 4, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 4863, "turns": 4, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784442557.7128403, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:fix-failing-test:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T02:29:24", "harness": "pi", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 10.099, "t_env_setup_s": 0.001, "t_agent_s": 10.099, "t_checker_s": 0.244, "tokens": 4863, "tokens_input_uncached": 4653, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 210, "tokens_reasoning": 17, "usage_raw": [{"input": 1172, "output": 83, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1255, "cost": {"input": 0.005860000000000001, "output": 0.00249, "cacheRead": 0, "cacheWrite": 0, "total": 0.00835}}, {"input": 1596, "output": 67, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1663, "cost": {"input": 0.007980000000000001, "output": 0.00201, "cacheRead": 0, "cacheWrite": 0, "total": 0.00999}}, {"input": 1684, "output": 26, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1710, "cost": {"input": 0.00842, "output": 0.00078, "cacheRead": 0, "cacheWrite": 0, "total": 0.0092}}, {"input": 201, "output": 34, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1771, "cost": {"input": 0.001005, "output": 0.00102, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.002793}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 4653, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 210, "tokens_proxy_reasoning": 17, "tokens_proxy_calls": 4, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 4863, "turns": 4, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784442570.969316, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:make-ci-green:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T02:29:34", "harness": "pi", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 34.873, "t_env_setup_s": 0.005, "t_agent_s": 34.873, "t_checker_s": 0.244, "tokens": 13758, "tokens_input_uncached": 12899, "tokens_cache_read": 34816, "tokens_cache_write": 0, "tokens_output": 859, "tokens_reasoning": 88, "usage_raw": [{"input": 1163, "output": 78, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1241, "cost": {"input": 0.005815000000000001, "output": 0.00234, "cacheRead": 0, "cacheWrite": 0, "total": 0.008155}}, {"input": 3036, "output": 236, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 3272, "cost": {"input": 0.01518, "output": 0.00708, "cacheRead": 0, "cacheWrite": 0, "total": 0.022260000000000002}}, {"input": 2506, "output": 138, "cacheRead": 2560, "cacheWrite": 0, "reasoning": 72, "totalTokens": 5204, "cost": {"input": 0.012530000000000001, "output": 0.0041400000000000005, "cacheRead": 0.0012799999999999999, "cacheWrite": 0, "total": 0.01795}}, {"input": 618, "output": 93, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5319, "cost": {"input": 0.0030900000000000003, "output": 0.00279, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.008184}}, {"input": 734, "output": 61, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5403, "cost": {"input": 0.00367, "output": 0.00183, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.007804}}, {"input": 818, "output": 65, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5491, "cost": {"input": 0.004090000000000001, "output": 0.0019500000000000001, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.008344}}, {"input": 906, "output": 49, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5563, "cost": {"input": 0.00453, "output": 0.00147, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.008304}}, {"input": 978, "output": 43, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5629, "cost": {"input": 0.00489, "output": 0.0012900000000000001, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.008484}}, {"input": 1043, "output": 26, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5677, "cost": {"input": 0.005215, "output": 0.00078, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.008299}}, {"input": 1097, "output": 70, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5775, "cost": {"input": 0.005485, "output": 0.0021, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.009889}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 12899, "tokens_proxy_cache_read": 34816, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 859, "tokens_proxy_reasoning": 88, "tokens_proxy_calls": 10, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 13758, "turns": 10, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784442588.0991874, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784442590.7150471, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784442594.2148976, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784442596.6184742, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784442599.5757415, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784442602.7162457, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:make-ci-green:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T02:30:09", "harness": "pi", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 35.51, 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0.01926}}, {"input": 1106, "output": 26, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5740, "cost": {"input": 0.00553, "output": 0.00078, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.008614}}, {"input": 136, "output": 74, "cacheRead": 5632, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5842, "cost": {"input": 0.00068, "output": 0.00222, "cacheRead": 0.002816, "cacheWrite": 0, "total": 0.005716000000000001}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 21370, "tokens_proxy_cache_read": 26624, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 911, "tokens_proxy_reasoning": 136, "tokens_proxy_calls": 10, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 22281, "turns": 10, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784442625.293117, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784442640.0254366, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784442629.4635954, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784442636.9715152, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784442631.4104269, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784442634.1784685, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:make-ci-green:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T02:30:45", "harness": "pi", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 41.399, 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"cacheWrite": 0, "reasoning": 0, "totalTokens": 5695, "cost": {"input": 0.005305000000000001, "output": 0.00078, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.008389}}, {"input": 1115, "output": 68, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 0, "totalTokens": 5791, "cost": {"input": 0.005575, "output": 0.00204, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.009919}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 12892, "tokens_proxy_cache_read": 34816, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 875, "tokens_proxy_reasoning": 106, "tokens_proxy_calls": 10, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 13767, "turns": 10, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784442660.4045942, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784442678.7991726, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784442671.2913651, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784442674.9501455, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784442664.5814648, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784442683.1264894, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:make-it-run:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T02:31:27", "harness": "pi", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 14.955, "t_env_setup_s": 0.002, "t_agent_s": 14.955, "t_checker_s": 0.247, "tokens": 8920, "tokens_input_uncached": 8668, "tokens_cache_read": 0, "tokens_cache_write": 0, "tokens_output": 252, "tokens_reasoning": 28, "usage_raw": [{"input": 1143, "output": 73, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1216, "cost": {"input": 0.0057150000000000005, "output": 0.00219, "cacheRead": 0, "cacheWrite": 0, "total": 0.007905}}, {"input": 1384, "output": 29, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1413, "cost": {"input": 0.006920000000000001, "output": 0.00087, "cacheRead": 0, "cacheWrite": 0, "total": 0.007790000000000001}}, {"input": 1438, "output": 54, "cacheRead": 0, "cacheWrite": 0, "reasoning": 12, "totalTokens": 1492, "cost": {"input": 0.00719, "output": 0.0016200000000000001, "cacheRead": 0, "cacheWrite": 0, "total": 0.00881}}, {"input": 1513, "output": 40, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1553, "cost": {"input": 0.0075650000000000005, "output": 0.0012000000000000001, "cacheRead": 0, "cacheWrite": 0, "total": 0.008765}}, {"input": 1575, "output": 25, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1600, "cost": {"input": 0.007875, "output": 0.00075, "cacheRead": 0, "cacheWrite": 0, "total": 0.008625}}, {"input": 1615, "output": 31, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1646, "cost": {"input": 0.008075, "output": 0.00093, "cacheRead": 0, "cacheWrite": 0, "total": 0.009005}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 8668, "tokens_proxy_cache_read": 0, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 252, "tokens_proxy_reasoning": 28, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8920, "turns": 6, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784442698.918292, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784442696.9273784, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:make-it-run:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T02:31:43", "harness": "pi", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 15.028, "t_env_setup_s": 0.001, "t_agent_s": 15.028, "t_checker_s": 0.247, "tokens": 8923, "tokens_input_uncached": 8669, "tokens_cache_read": 0, "tokens_cache_write": 0, "tokens_output": 254, "tokens_reasoning": 30, "usage_raw": [{"input": 1142, "output": 73, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1215, "cost": {"input": 0.005710000000000001, "output": 0.00219, "cacheRead": 0, "cacheWrite": 0, "total": 0.0079}}, {"input": 1383, "output": 30, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1413, "cost": {"input": 0.006915, "output": 0.0009, "cacheRead": 0, "cacheWrite": 0, "total": 0.007815}}, {"input": 1438, "output": 55, "cacheRead": 0, "cacheWrite": 0, "reasoning": 13, "totalTokens": 1493, "cost": {"input": 0.00719, "output": 0.00165, "cacheRead": 0, "cacheWrite": 0, "total": 0.00884}}, {"input": 1514, "output": 40, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1554, "cost": {"input": 0.00757, "output": 0.0012000000000000001, "cacheRead": 0, "cacheWrite": 0, "total": 0.00877}}, {"input": 1576, "output": 25, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1601, "cost": {"input": 0.00788, "output": 0.00075, "cacheRead": 0, "cacheWrite": 0, "total": 0.00863}}, {"input": 1616, "output": 31, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1647, "cost": {"input": 0.00808, "output": 0.00093, "cacheRead": 0, "cacheWrite": 0, "total": 0.00901}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 8669, "tokens_proxy_cache_read": 0, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 254, "tokens_proxy_reasoning": 30, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8923, "turns": 6, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784442714.3945968, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784442712.3027346, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:make-it-run:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T02:31:58", "harness": "pi", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 15.337, "t_env_setup_s": 0.001, "t_agent_s": 15.337, "t_checker_s": 0.204, "tokens": 7933, "tokens_input_uncached": 7657, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 276, "tokens_reasoning": 17, "usage_raw": [{"input": 1142, "output": 80, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1222, "cost": {"input": 0.005710000000000001, "output": 0.0024000000000000002, "cacheRead": 0, "cacheWrite": 0, "total": 0.008110000000000001}}, {"input": 1447, "output": 49, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1496, "cost": {"input": 0.007235000000000001, "output": 0.00147, "cacheRead": 0, "cacheWrite": 0, "total": 0.008705000000000001}}, {"input": 1556, "output": 51, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1607, "cost": {"input": 0.0077800000000000005, "output": 0.0015300000000000001, "cacheRead": 0, "cacheWrite": 0, "total": 0.00931}}, {"input": 1628, "output": 40, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1668, "cost": {"input": 0.008140000000000001, "output": 0.0012000000000000001, "cacheRead": 0, "cacheWrite": 0, "total": 0.009340000000000001}}, {"input": 154, "output": 25, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1715, "cost": {"input": 0.0007700000000000001, "output": 0.00075, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.002288}}, {"input": 1730, "output": 31, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1761, "cost": {"input": 0.008650000000000001, "output": 0.00093, "cacheRead": 0, "cacheWrite": 0, "total": 0.009580000000000002}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 7657, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 276, "tokens_proxy_reasoning": 17, "tokens_proxy_calls": 6, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7933, "turns": 6, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784442729.9727104, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784442727.735215, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:misleading-error:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T02:32:14", "harness": "pi", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 15.674, "t_env_setup_s": 0.002, "t_agent_s": 15.674, "t_checker_s": 0.246, "tokens": 5283, "tokens_input_uncached": 4974, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 309, "tokens_reasoning": 24, "usage_raw": [{"input": 1224, "output": 97, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1321, "cost": {"input": 0.0061200000000000004, "output": 0.0029100000000000003, "cacheRead": 0, "cacheWrite": 0, "total": 0.00903}}, {"input": 1463, "output": 82, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1545, "cost": {"input": 0.007315, "output": 0.00246, "cacheRead": 0, "cacheWrite": 0, "total": 0.009775}}, {"input": 1716, "output": 63, "cacheRead": 0, "cacheWrite": 0, "reasoning": 16, "totalTokens": 1779, "cost": {"input": 0.00858, "output": 0.00189, "cacheRead": 0, "cacheWrite": 0, "total": 0.01047}}, {"input": 264, "output": 25, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1825, "cost": {"input": 0.0013200000000000002, "output": 0.00075, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.0028380000000000002}}, {"input": 307, "output": 42, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1885, "cost": {"input": 0.0015350000000000001, "output": 0.00126, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.003563}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 4974, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 309, "tokens_proxy_reasoning": 24, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 5283, "turns": 5, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784442746.3800087, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:misleading-error:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T02:32:30", "harness": "pi", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 14.823, "t_env_setup_s": 0.001, "t_agent_s": 14.823, "t_checker_s": 0.244, "tokens": 6799, "tokens_input_uncached": 6497, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 302, "tokens_reasoning": 23, "usage_raw": [{"input": 1225, "output": 93, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1318, "cost": {"input": 0.006125, "output": 0.00279, "cacheRead": 0, "cacheWrite": 0, "total": 0.008915}}, {"input": 1460, "output": 82, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1542, "cost": {"input": 0.007300000000000001, "output": 0.00246, "cacheRead": 0, "cacheWrite": 0, "total": 0.009760000000000001}}, {"input": 1713, "output": 62, "cacheRead": 0, "cacheWrite": 0, "reasoning": 15, "totalTokens": 1775, "cost": {"input": 0.008565000000000001, "output": 0.00186, "cacheRead": 0, "cacheWrite": 0, "total": 0.010425000000000002}}, {"input": 1796, "output": 25, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1821, "cost": {"input": 0.00898, "output": 0.00075, "cacheRead": 0, "cacheWrite": 0, "total": 0.00973}}, {"input": 303, "output": 40, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1879, "cost": {"input": 0.001515, "output": 0.0012000000000000001, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.0034830000000000004}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 6497, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 302, "tokens_proxy_reasoning": 23, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 6799, "turns": 5, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784442761.6735845, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:misleading-error:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T02:32:45", "harness": "pi", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 12.46, "t_env_setup_s": 0.002, "t_agent_s": 12.46, "t_checker_s": 0.204, "tokens": 7403, "tokens_input_uncached": 7085, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 318, "tokens_reasoning": 34, "usage_raw": [{"input": 1223, "output": 97, "cacheRead": 0, "cacheWrite": 0, "reasoning": 8, "totalTokens": 1320, "cost": {"input": 0.006115000000000001, "output": 0.0029100000000000003, "cacheRead": 0, "cacheWrite": 0, "total": 0.009025000000000002}}, {"input": 1602, "output": 82, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1684, "cost": {"input": 0.008010000000000001, "output": 0.00246, "cacheRead": 0, "cacheWrite": 0, "total": 0.010470000000000002}}, {"input": 1855, "output": 73, "cacheRead": 0, "cacheWrite": 0, "reasoning": 26, "totalTokens": 1928, "cost": {"input": 0.009275, "output": 0.00219, "cacheRead": 0, "cacheWrite": 0, "total": 0.011465}}, {"input": 413, "output": 25, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 0, "totalTokens": 1974, "cost": {"input": 0.002065, "output": 0.00075, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.003583}}, {"input": 1992, "output": 41, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 2033, "cost": {"input": 0.00996, "output": 0.00123, "cacheRead": 0, "cacheWrite": 0, "total": 0.01119}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 7085, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 318, "tokens_proxy_reasoning": 34, "tokens_proxy_calls": 5, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7403, "turns": 5, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784442774.5342877, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:taskflow:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T02:32:58", "harness": "pi", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 37.942, "t_env_setup_s": 0.011, "t_agent_s": 37.942, "t_checker_s": 0.237, "tokens": 37372, "tokens_input_uncached": 36268, "tokens_cache_read": 120832, "tokens_cache_write": 0, "tokens_output": 1104, "tokens_reasoning": 164, "usage_raw": [{"input": 1427, "output": 37, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1464, "cost": {"input": 0.007135000000000001, "output": 0.00111, "cacheRead": 0, "cacheWrite": 0, "total": 0.008245}}, {"input": 4118, "output": 122, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 4240, "cost": {"input": 0.02059, "output": 0.00366, "cacheRead": 0, "cacheWrite": 0, "total": 0.02425}}, {"input": 4694, "output": 162, "cacheRead": 3584, "cacheWrite": 0, "reasoning": 8, "totalTokens": 8440, "cost": {"input": 0.02347, "output": 0.00486, "cacheRead": 0.001792, "cacheWrite": 0, "total": 0.030122}}, {"input": 14040, "output": 127, "cacheRead": 7680, "cacheWrite": 0, "reasoning": 23, "totalTokens": 21847, "cost": {"input": 0.07020000000000001, "output": 0.00381, "cacheRead": 0.0038399999999999997, "cacheWrite": 0, "total": 0.07785}}, {"input": 9056, "output": 108, "cacheRead": 20992, "cacheWrite": 0, "reasoning": 49, "totalTokens": 30156, "cost": {"input": 0.04528, "output": 0.0032400000000000003, "cacheRead": 0.010496, "cacheWrite": 0, "total": 0.059016}}, {"input": 995, "output": 326, "cacheRead": 29184, "cacheWrite": 0, "reasoning": 0, "totalTokens": 30505, "cost": {"input": 0.004975, "output": 0.00978, "cacheRead": 0.014591999999999999, "cacheWrite": 0, "total": 0.029346999999999998}}, {"input": 1454, "output": 26, "cacheRead": 29184, "cacheWrite": 0, "reasoning": 0, "totalTokens": 30664, "cost": {"input": 0.00727, "output": 0.00078, "cacheRead": 0.014591999999999999, "cacheWrite": 0, "total": 0.022642}}, {"input": 484, "output": 196, "cacheRead": 30208, "cacheWrite": 0, "reasoning": 66, "totalTokens": 30888, "cost": {"input": 0.0024200000000000003, "output": 0.00588, "cacheRead": 0.015104, "cacheWrite": 0, "total": 0.023404}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 36268, "tokens_proxy_cache_read": 120832, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1104, "tokens_proxy_reasoning": 164, "tokens_proxy_calls": 8, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 37372, "turns": 8, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, 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1784442807.283391, "sha256": ""}, "taskflow/runner.py": {"bytes": 4452, "mtime": 1784160991.2259035, "sha256": ""}, "taskflow/scheduler.py": {"bytes": 14635, "mtime": 1784442807.2865894, "sha256": ""}, "taskflow/serialization.py": {"bytes": 9103, "mtime": 1784160991.2260818, "sha256": ""}, "taskflow/simulation.py": {"bytes": 7494, "mtime": 1784160991.226148, "sha256": ""}, "taskflow/statemachine.py": {"bytes": 6500, "mtime": 1784442807.287407, "sha256": ""}, "taskflow/stats.py": {"bytes": 12341, "mtime": 1784160991.226304, "sha256": ""}, "taskflow/templating.py": {"bytes": 8884, "mtime": 1784160991.2263978, "sha256": ""}, "taskflow/timeutil.py": {"bytes": 9180, "mtime": 1784160991.2264779, "sha256": ""}, "taskflow/validation.py": {"bytes": 12447, "mtime": 1784160991.2265797, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:taskflow:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T02:33:36", "harness": "pi", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 45.15, "t_env_setup_s": 0.006, "t_agent_s": 45.15, "t_checker_s": 0.239, "tokens": 64025, "tokens_input_uncached": 63063, "tokens_cache_read": 209920, "tokens_cache_write": 0, "tokens_output": 962, "tokens_reasoning": 70, "usage_raw": [{"input": 1429, "output": 37, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1466, "cost": {"input": 0.007145, "output": 0.00111, "cacheRead": 0, "cacheWrite": 0, "total": 0.008255}}, {"input": 4156, "output": 86, "cacheRead": 0, "cacheWrite": 0, "reasoning": 10, "totalTokens": 4242, "cost": {"input": 0.020780000000000003, "output": 0.0025800000000000003, "cacheRead": 0, "cacheWrite": 0, "total": 0.023360000000000002}}, {"input": 4788, "output": 136, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 4924, "cost": {"input": 0.023940000000000003, "output": 0.00408, "cacheRead": 0, "cacheWrite": 0, "total": 0.028020000000000003}}, 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"cacheRead": 0.013568, "cacheWrite": 0, "total": 0.015553}}, {"input": 1319, "output": 82, "cacheRead": 26112, "cacheWrite": 0, "reasoning": 0, "totalTokens": 27513, "cost": {"input": 0.006595, "output": 0.00246, "cacheRead": 0.013056, "cacheWrite": 0, "total": 0.022111}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 63063, "tokens_proxy_cache_read": 209920, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 962, "tokens_proxy_reasoning": 70, "tokens_proxy_calls": 13, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 64025, "turns": 13, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, 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1784442840.8676064, "sha256": ""}, "taskflow/runner.py": {"bytes": 4452, "mtime": 1784160991.2259035, "sha256": ""}, "taskflow/scheduler.py": {"bytes": 14635, "mtime": 1784442850.816687, "sha256": ""}, "taskflow/serialization.py": {"bytes": 9103, "mtime": 1784160991.2260818, "sha256": ""}, "taskflow/simulation.py": {"bytes": 7494, "mtime": 1784160991.226148, "sha256": ""}, "taskflow/statemachine.py": {"bytes": 6500, "mtime": 1784442843.0696037, "sha256": ""}, "taskflow/stats.py": {"bytes": 12341, "mtime": 1784160991.226304, "sha256": ""}, "taskflow/templating.py": {"bytes": 8884, "mtime": 1784160991.2263978, "sha256": ""}, "taskflow/timeutil.py": {"bytes": 9180, "mtime": 1784160991.2264779, "sha256": ""}, "taskflow/validation.py": {"bytes": 12447, "mtime": 1784160991.2265797, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "pi:taskflow:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T02:34:22", "harness": "pi", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 45.747, "t_env_setup_s": 0.007, "t_agent_s": 45.747, "t_checker_s": 0.24, "tokens": 36633, "tokens_input_uncached": 35553, "tokens_cache_read": 239104, "tokens_cache_write": 0, "tokens_output": 1080, "tokens_reasoning": 135, "usage_raw": [{"input": 1429, "output": 37, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1466, "cost": {"input": 0.007145, "output": 0.00111, "cacheRead": 0, "cacheWrite": 0, "total": 0.008255}}, {"input": 4156, "output": 85, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 4241, "cost": {"input": 0.020780000000000003, "output": 0.00255, "cacheRead": 0, "cacheWrite": 0, "total": 0.023330000000000004}}, {"input": 1203, "output": 154, "cacheRead": 3584, "cacheWrite": 0, "reasoning": 0, "totalTokens": 4941, "cost": {"input": 0.006015, "output": 0.00462, "cacheRead": 0.001792, "cacheWrite": 0, "total": 0.012427}}, {"input": 15241, 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0.015708}}, {"input": 326, "output": 119, "cacheRead": 27136, "cacheWrite": 0, "reasoning": 0, "totalTokens": 27581, "cost": {"input": 0.0016300000000000002, "output": 0.0035700000000000003, "cacheRead": 0.013568, "cacheWrite": 0, "total": 0.018768}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 35553, "tokens_proxy_cache_read": 239104, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1080, "tokens_proxy_reasoning": 135, "tokens_proxy_calls": 13, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 36633, "turns": 13, "cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, 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"--thinking", "medium", "--mode", "json", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, "webcore/__init__.py": {"bytes": 2152, "mtime": 1784443116.776836, 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layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, "webcore/__init__.py": {"bytes": 2170, "mtime": 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"--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "0.80.10 (/opt/homebrew/bin/pi)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, "webcore/__init__.py": {"bytes": 2152, "mtime": 1784443580.835995, 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When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784443773.671086, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784443767.5370677, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784443773.6708896, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "pi:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T02:49:39", "harness": "pi", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 57.669, "t_env_setup_s": 0.087, "t_agent_s": 57.475, "t_checker_s": 0.234, "tokens": 16616, "tokens_input_uncached": 14739, "tokens_cache_read": 20480, "tokens_cache_write": 0, "tokens_output": 1877, "tokens_reasoning": 778, "usage_raw": [{"input": 1221, "output": 39, "cacheRead": 0, "cacheWrite": 0, "reasoning": 9, "totalTokens": 1260, "cost": {"input": 0.006105, "output": 0.00117, "cacheRead": 0, "cacheWrite": 0, "total": 0.007275}}, {"input": 1373, "output": 173, "cacheRead": 0, "cacheWrite": 0, "reasoning": 32, "totalTokens": 1546, "cost": {"input": 0.0068650000000000004, "output": 0.00519, "cacheRead": 0, "cacheWrite": 0, "total": 0.012055}}, {"input": 1683, "output": 233, "cacheRead": 0, "cacheWrite": 0, "reasoning": 58, "totalTokens": 1916, "cost": {"input": 0.008415, "output": 0.0069900000000000006, "cacheRead": 0, "cacheWrite": 0, "total": 0.015405000000000002}}, {"input": 696, "output": 41, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 19, "totalTokens": 2273, "cost": {"input": 0.0034800000000000005, "output": 0.00123, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.005478}}, {"input": 817, "output": 137, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 60, "totalTokens": 2490, "cost": {"input": 0.004085, "output": 0.00411, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.008963}}, {"input": 4003, "output": 119, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 40, "totalTokens": 5658, "cost": {"input": 0.020015, "output": 0.0035700000000000003, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.024353000000000003}}, {"input": 1858, "output": 99, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 19, "totalTokens": 6565, "cost": {"input": 0.009290000000000001, "output": 0.00297, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.014564000000000002}}, {"input": 980, "output": 929, "cacheRead": 5632, "cacheWrite": 0, "reasoning": 476, "totalTokens": 7541, "cost": {"input": 0.004900000000000001, "output": 0.027870000000000002, "cacheRead": 0.002816, "cacheWrite": 0, "total": 0.035586}}, {"input": 2108, "output": 107, "cacheRead": 5632, "cacheWrite": 0, "reasoning": 65, "totalTokens": 7847, "cost": {"input": 0.01054, "output": 0.00321, "cacheRead": 0.002816, "cacheWrite": 0, "total": 0.016566}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 14739, "tokens_proxy_cache_read": 20480, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1877, "tokens_proxy_reasoning": 778, "tokens_proxy_calls": 9, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 16616, "turns": 9, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_pi_17361_775db4c1", "-v", "/dev/openbench/.bench-tmp/bench_pi_terminal-bench_db-wal-recovery_ia3nti2g:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_oqq6i3_1.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:57747", "-e", "OPENBENCH_PROXY_CELL_TOKEN=4H5k_H87rYC4nbE6H7jZ2LQV", "-v", "/.pi/agent/auth.json:/bench/auth/.pi/agent/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "pi", "gpt-5.6-sol", "2400"], "adapter_cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: recovered rows do not match the checker-owned oracle\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784443831.6979823, "sha256": ""}, "main.db-wal": {"bytes": 4152, "mtime": 1784443831.6983562, "sha256": ""}, "recovered.json": {"bytes": 472, "mtime": 1784443831.698888, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "pi:terminal-bench/extract-elf:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T02:50:38", "harness": "pi", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 71.341, "t_env_setup_s": 0.085, "t_agent_s": 71.146, "t_checker_s": 0.289, "tokens": 16423, "tokens_input_uncached": 13216, "tokens_cache_read": 24064, "tokens_cache_write": 0, "tokens_output": 3207, "tokens_reasoning": 1162, "usage_raw": [{"input": 1193, "output": 133, "cacheRead": 0, "cacheWrite": 0, "reasoning": 32, "totalTokens": 1326, "cost": {"input": 0.005965000000000001, "output": 0.0039900000000000005, "cacheRead": 0, "cacheWrite": 0, "total": 0.009955000000000002}}, {"input": 1416, "output": 118, "cacheRead": 0, "cacheWrite": 0, "reasoning": 17, "totalTokens": 1534, "cost": {"input": 0.00708, "output": 0.00354, "cacheRead": 0, "cacheWrite": 0, "total": 0.010620000000000001}}, {"input": 1873, "output": 397, "cacheRead": 0, "cacheWrite": 0, "reasoning": 54, "totalTokens": 2270, "cost": {"input": 0.009365, "output": 0.01191, "cacheRead": 0, "cacheWrite": 0, "total": 0.021275000000000002}}, {"input": 2213, "output": 332, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 42, "totalTokens": 4081, "cost": {"input": 0.011065, "output": 0.00996, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.021793000000000003}}, {"input": 2134, "output": 138, "cacheRead": 3584, "cacheWrite": 0, "reasoning": 49, "totalTokens": 5856, "cost": {"input": 0.01067, "output": 0.0041400000000000005, "cacheRead": 0.001792, "cacheWrite": 0, "total": 0.016602}}, {"input": 767, "output": 1952, "cacheRead": 5632, "cacheWrite": 0, "reasoning": 958, "totalTokens": 8351, "cost": {"input": 0.0038350000000000003, "output": 0.05856, "cacheRead": 0.002816, "cacheWrite": 0, "total": 0.065211}}, {"input": 2738, "output": 94, "cacheRead": 5632, "cacheWrite": 0, "reasoning": 10, "totalTokens": 8464, "cost": {"input": 0.01369, "output": 0.00282, "cacheRead": 0.002816, "cacheWrite": 0, "total": 0.019326}}, {"input": 882, "output": 43, "cacheRead": 7680, "cacheWrite": 0, "reasoning": 0, "totalTokens": 8605, "cost": {"input": 0.004410000000000001, "output": 0.0012900000000000001, "cacheRead": 0.0038399999999999997, "cacheWrite": 0, "total": 0.00954}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 13216, "tokens_proxy_cache_read": 24064, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3207, "tokens_proxy_reasoning": 1162, "tokens_proxy_calls": 8, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 16423, "turns": 8, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_pi_17361_840e6e77", "-v", "/dev/openbench/.bench-tmp/bench_pi_terminal-bench_extract-elf_hsj_zagm:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_liqva_2o.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:57747", "-e", "OPENBENCH_PROXY_CELL_TOKEN=qvAQINOdQWGS3wwCVf7ShHS6", "-v", "/.pi/agent/auth.json:/bench/auth/.pi/agent/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "pi", "gpt-5.6-sol", "2400"], "adapter_cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 2831, "mtime": 1784443903.596034, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "pi:terminal-bench/extract-elf:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T02:51:52", "harness": "pi", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 166.154, "t_env_setup_s": 0.083, "t_agent_s": 165.884, "t_checker_s": 0.289, "tokens": 29375, "tokens_input_uncached": 23522, "tokens_cache_read": 77312, "tokens_cache_write": 0, "tokens_output": 5853, "tokens_reasoning": 2829, "usage_raw": [{"input": 1193, "output": 126, "cacheRead": 0, "cacheWrite": 0, "reasoning": 30, "totalTokens": 1319, "cost": {"input": 0.005965000000000001, "output": 0.00378, "cacheRead": 0, "cacheWrite": 0, "total": 0.009745}}, {"input": 1401, "output": 183, "cacheRead": 0, "cacheWrite": 0, "reasoning": 17, "totalTokens": 1584, "cost": {"input": 0.007005000000000001, "output": 0.00549, "cacheRead": 0, "cacheWrite": 0, "total": 0.012495000000000001}}, {"input": 1754, "output": 313, "cacheRead": 0, "cacheWrite": 0, "reasoning": 67, "totalTokens": 2067, "cost": {"input": 0.00877, "output": 0.00939, "cacheRead": 0, "cacheWrite": 0, "total": 0.018160000000000003}}, {"input": 1882, "output": 141, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 85, 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"/.pi/agent/auth.json:/bench/auth/.pi/agent/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "pi", "gpt-5.6-sol", "2400"], "adapter_cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. 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It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. 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Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. 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Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. 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Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. 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This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? 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This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? 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system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784444775.5712013, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 88800, "mtime": 1784444775.5761743, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "pi:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T03:06:30", "harness": "pi", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 75.982, "t_env_setup_s": 0.086, "t_agent_s": 75.779, "t_checker_s": 0.243, "tokens": 39840, "tokens_input_uncached": 36409, "tokens_cache_read": 60416, "tokens_cache_write": 0, "tokens_output": 3431, "tokens_reasoning": 1239, "usage_raw": [{"input": 319, "output": 143, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 13, "totalTokens": 1998, "cost": {"input": 0.001595, "output": 0.00429, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.006653000000000001}}, {"input": 4013, "output": 1051, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 358, "totalTokens": 6600, "cost": {"input": 0.020065000000000003, "output": 0.03153, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.052363}}, {"input": 6622, "output": 27, "cacheRead": 0, "cacheWrite": 0, "reasoning": 0, "totalTokens": 6649, "cost": {"input": 0.03311, "output": 0.0008100000000000001, "cacheRead": 0, "cacheWrite": 0, "total": 0.03392}}, {"input": 1047, "output": 28, "cacheRead": 5632, "cacheWrite": 0, "reasoning": 0, "totalTokens": 6707, "cost": {"input": 0.005235, "output": 0.00084, "cacheRead": 0.002816, "cacheWrite": 0, "total": 0.008891}}, {"input": 4040, "output": 1071, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 648, "totalTokens": 9719, "cost": {"input": 0.020200000000000003, "output": 0.03213, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.054634}}, {"input": 4111, "output": 247, "cacheRead": 5632, "cacheWrite": 0, "reasoning": 41, "totalTokens": 9990, "cost": {"input": 0.020555, "output": 0.00741, "cacheRead": 0.002816, "cacheWrite": 0, "total": 0.030781}}, {"input": 1312, "output": 407, "cacheRead": 8704, "cacheWrite": 0, "reasoning": 0, "totalTokens": 10423, "cost": {"input": 0.006560000000000001, "output": 0.01221, "cacheRead": 0.004352, "cacheWrite": 0, "total": 0.023122000000000004}}, {"input": 13960, "output": 302, "cacheRead": 9728, "cacheWrite": 0, "reasoning": 179, "totalTokens": 23990, "cost": {"input": 0.0698, "output": 0.00906, "cacheRead": 0.004863999999999999, "cacheWrite": 0, "total": 0.08372399999999999}}, {"input": 985, "output": 155, "cacheRead": 23040, "cacheWrite": 0, "reasoning": 0, "totalTokens": 24180, "cost": {"input": 0.0049250000000000006, "output": 0.0046500000000000005, "cacheRead": 0.011519999999999999, "cacheWrite": 0, "total": 0.021095}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 36409, "tokens_proxy_cache_read": 60416, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3431, "tokens_proxy_reasoning": 1239, "tokens_proxy_calls": 9, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 39840, "turns": 9, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_pi_17361_7da343f0", "-v", "/dev/openbench/.bench-tmp/bench_pi_terminal-bench_llm-inference-batching-scheduler_6ryazxh5:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_exhlh1rd.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:57747", "-e", "OPENBENCH_PROXY_CELL_TOKEN=ktwOM_0Wr3dn33VTLV8-WkaK", "-v", "/.pi/agent/auth.json:/bench/auth/.pi/agent/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "pi", "gpt-5.6-sol", "2400"], "adapter_cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784444854.8351333, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 88800, "mtime": 1784444854.839681, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "pi:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T03:07:46", "harness": "pi", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 1175.495, "t_env_setup_s": 0.082, "t_agent_s": 1175.236, "t_checker_s": 0.231, "tokens": 42136, "tokens_input_uncached": 34713, "tokens_cache_read": 120832, "tokens_cache_write": 0, "tokens_output": 7423, "tokens_reasoning": 2644, "usage_raw": [{"input": 1855, "output": 143, "cacheRead": 0, "cacheWrite": 0, "reasoning": 13, "totalTokens": 1998, "cost": {"input": 0.009275, "output": 0.00429, "cacheRead": 0, "cacheWrite": 0, "total": 0.013565}}, {"input": 4013, "output": 570, "cacheRead": 1536, "cacheWrite": 0, "reasoning": 301, "totalTokens": 6119, "cost": {"input": 0.020065000000000003, "output": 0.0171, "cacheRead": 0.000768, "cacheWrite": 0, "total": 0.037933}}, {"input": 1733, "output": 549, "cacheRead": 4608, "cacheWrite": 0, "reasoning": 184, "totalTokens": 6890, "cost": {"input": 0.008665, "output": 0.016470000000000002, "cacheRead": 0.002304, "cacheWrite": 0, "total": 0.027439000000000005}}, {"input": 1682, "output": 2567, "cacheRead": 5632, "cacheWrite": 0, "reasoning": 1438, "totalTokens": 9881, "cost": {"input": 0.00841, "output": 0.07701, "cacheRead": 0.002816, "cacheWrite": 0, "total": 0.088236}}, {"input": 9903, "output": 76, "cacheRead": 0, "cacheWrite": 0, "reasoning": 44, "totalTokens": 9979, "cost": {"input": 0.049515, "output": 0.00228, "cacheRead": 0, "cacheWrite": 0, "total": 0.051795}}, {"input": 495, "output": 482, "cacheRead": 9728, "cacheWrite": 0, "reasoning": 136, "totalTokens": 10705, "cost": {"input": 0.002475, "output": 0.01446, "cacheRead": 0.004863999999999999, "cacheWrite": 0, "total": 0.021799000000000002}}, {"input": 1001, "output": 30, "cacheRead": 9728, "cacheWrite": 0, "reasoning": 0, "totalTokens": 10759, "cost": {"input": 0.005005000000000001, "output": 0.0009, "cacheRead": 0.004863999999999999, "cacheWrite": 0, "total": 0.010769}}, {"input": 1362, "output": 308, "cacheRead": 9728, "cacheWrite": 0, "reasoning": 42, "totalTokens": 11398, "cost": {"input": 0.006810000000000001, "output": 0.00924, "cacheRead": 0.004863999999999999, "cacheWrite": 0, "total": 0.020914000000000002}}, {"input": 736, "output": 480, "cacheRead": 10752, "cacheWrite": 0, "reasoning": 74, "totalTokens": 11968, "cost": {"input": 0.00368, "output": 0.0144, "cacheRead": 0.005376, "cacheWrite": 0, "total": 0.023455999999999998}}, {"input": 1558, "output": 436, "cacheRead": 10752, "cacheWrite": 0, "reasoning": 112, "totalTokens": 12746, "cost": {"input": 0.007790000000000001, "output": 0.01308, "cacheRead": 0.005376, "cacheWrite": 0, "total": 0.026246}}, {"input": 1119, "output": 1016, "cacheRead": 11776, "cacheWrite": 0, "reasoning": 122, "totalTokens": 13911, "cost": {"input": 0.005595, "output": 0.03048, "cacheRead": 0.005888, "cacheWrite": 0, "total": 0.041963}}, {"input": 1133, "output": 81, "cacheRead": 12800, "cacheWrite": 0, "reasoning": 45, "totalTokens": 14014, "cost": {"input": 0.005665000000000001, "output": 0.00243, "cacheRead": 0.0063999999999999994, "cacheWrite": 0, "total": 0.014495000000000001}}, {"input": 6717, "output": 622, "cacheRead": 13824, "cacheWrite": 0, "reasoning": 133, "totalTokens": 21163, "cost": {"input": 0.033585000000000004, "output": 0.01866, "cacheRead": 0.006912, "cacheWrite": 0, "total": 0.059157}}, {"input": 1406, "output": 63, "cacheRead": 19968, "cacheWrite": 0, "reasoning": 0, "totalTokens": 21437, "cost": {"input": 0.007030000000000001, "output": 0.00189, "cacheRead": 0.009984, "cacheWrite": 0, "total": 0.018904}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": 34713, "tokens_proxy_cache_read": 120832, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 7423, "tokens_proxy_reasoning": 2644, "tokens_proxy_calls": 14, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 42136, "turns": 14, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_pi_17361_271354f5", "-v", "/dev/openbench/.bench-tmp/bench_pi_terminal-bench_llm-inference-batching-scheduler_02kq6wqm:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_7axl_jvu.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:57747", "-e", "OPENBENCH_PROXY_CELL_TOKEN=I-rFQmVQjQkYp1DTVxWK7XEG", "-v", "/.pi/agent/auth.json:/bench/auth/.pi/agent/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "pi", "gpt-5.6-sol", "2400"], "adapter_cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784446035.8774817, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 88800, "mtime": 1784446035.8826942, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 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It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 207, "mtime": 1784446195.7109969, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "pi:terminal-bench/raman-fitting:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T03:30:01", "harness": "pi", "model": "gpt-5.6-sol", "task": 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It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 215, "mtime": 1784446352.977083, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "pi:terminal-bench/raman-fitting:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T03:32:37", "harness": "pi", "model": "gpt-5.6-sol", 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"/dev/openbench/.bench-tmp/bench_instr_g1zn9up7.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:57747", "-e", "OPENBENCH_PROXY_CELL_TOKEN=lO38WUNx4zLBNMWzFCdLsgTz", "-v", "/.pi/agent/auth.json:/bench/auth/.pi/agent/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "pi", "gpt-5.6-sol", "2400"], "adapter_cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: 2D parameters outside tolerance: gamma, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 221, "mtime": 1784446585.430183, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false} {"run_id": "pi:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T03:36:32", "harness": "pi", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 1, "success": false, "completed": false, "error": "timeout after 2400s", "wall_time_s": 2405.36, "t_env_setup_s": 0.094, "t_agent_s": 2404.472, "t_checker_s": 120.035, "tokens": null, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 42596, "tokens_proxy_cache_read": 473600, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 14719, "tokens_proxy_reasoning": 6204, "tokens_proxy_calls": 25, "sampling_observed": [{"model": "gpt-5.6-sol", "provider": "openai-codex", "thinking": "medium"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": null, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_pi_17361_c401f013", "-v", "/dev/openbench/.bench-tmp/bench_pi_terminal-bench_schemelike-metacircular-eval_uzdy5ldy:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_qwodqz1k.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:57747", "-e", "OPENBENCH_PROXY_CELL_TOKEN=E4DhuSPz9exNNc3EzLKqHA4_", "-v", "/.pi/agent/auth.json:/bench/auth/.pi/agent/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "pi", "gpt-5.6-sol", "2400"], "adapter_cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": "timeout", "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "rate_limited", "checker_stdout": "", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784448605.9205487, "sha256": ""}, "eval.scm": {"bytes": 25217, "mtime": 1784447993.244783, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, 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"/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ob0r1u6e.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:57747", "-e", "OPENBENCH_PROXY_CELL_TOKEN=j3ieJ2af-bA6yj-qSkJirv5W", "-v", "/.pi/agent/auth.json:/bench/auth/.pi/agent/auth.json:ro", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "pi", "gpt-5.6-sol", "2400"], "adapter_cmd": ["pi", "-p", "--no-approve", "--provider", "openai-codex", "--model", "gpt-5.6-sol", "--thinking", "medium", "--mode", "json", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, 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1788, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 29145, "tokens_proxy_cache_read": 216064, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5202, "tokens_proxy_reasoning": 1788, "tokens_proxy_calls": 20, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 33356, "turns": 19, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_add-feature_g2qy47e7"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3548, "mtime": 1784452723.072457, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1764, "mtime": 1784452633.5397248, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:add-feature:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T05:19:08", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 157.246, "t_env_setup_s": 0.003, "t_agent_s": 157.246, "t_checker_s": 0.232, "tokens": 229885, "tokens_input_uncached": 33121, "tokens_cache_read": 190976, "tokens_cache_write": null, "tokens_output": 5788, "tokens_reasoning": 2360, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 34085, "tokens_proxy_cache_read": 190976, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5815, "tokens_proxy_reasoning": 2360, "tokens_proxy_calls": 19, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 38909, "turns": 18, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_add-feature_v0t__0ti"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3227, "mtime": 1784452857.1635737, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1477, "mtime": 1784452802.7757485, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:add-feature:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T05:21:46", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 156.764, "t_env_setup_s": 0.003, "t_agent_s": 156.764, "t_checker_s": 0.27, "tokens": 262588, "tokens_input_uncached": 46804, "tokens_cache_read": 210432, "tokens_cache_write": null, "tokens_output": 5352, "tokens_reasoning": 1816, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 47768, "tokens_proxy_cache_read": 210432, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5380, "tokens_proxy_reasoning": 1816, "tokens_proxy_calls": 22, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 52156, "turns": 21, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_add-feature_9yp7qt7o"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3416, "mtime": 1784453025.0631995, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1629, "mtime": 1784452973.9115703, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:build-a-cli:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T05:24:23", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 55.098, "t_env_setup_s": 0.001, "t_agent_s": 55.098, "t_checker_s": 0.291, "tokens": 98186, "tokens_input_uncached": 9255, "tokens_cache_read": 87552, "tokens_cache_write": null, "tokens_output": 1379, "tokens_reasoning": 311, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 10055, "tokens_proxy_cache_read": 87552, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1403, "tokens_proxy_reasoning": 311, "tokens_proxy_calls": 12, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 10634, "turns": 11, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_build-a-cli_kyepzrir"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1202, "mtime": 1784453092.1675363, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:build-a-cli:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T05:25:19", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 25.083, "t_env_setup_s": 0.001, "t_agent_s": 25.083, "t_checker_s": 0.228, "tokens": 34100, "tokens_input_uncached": 4758, "tokens_cache_read": 28672, "tokens_cache_write": null, "tokens_output": 670, "tokens_reasoning": 155, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 5558, "tokens_proxy_cache_read": 28672, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 694, "tokens_proxy_reasoning": 155, "tokens_proxy_calls": 5, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 5428, "turns": 4, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_build-a-cli_dyyvrvcs"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 845, "mtime": 1784453134.1808405, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:build-a-cli:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T05:25:44", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 24.365, "t_env_setup_s": 0.0, "t_agent_s": 24.365, "t_checker_s": 0.228, "tokens": 34236, "tokens_input_uncached": 4869, "tokens_cache_read": 28672, "tokens_cache_write": null, "tokens_output": 695, "tokens_reasoning": 95, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 5669, "tokens_proxy_cache_read": 28672, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 719, "tokens_proxy_reasoning": 95, "tokens_proxy_calls": 5, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 5564, "turns": 4, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_build-a-cli_p5bsuq66"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 937, "mtime": 1784453159.6993878, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:fix-failing-test:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T05:26:09", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 19.59, "t_env_setup_s": 0.002, "t_agent_s": 19.59, "t_checker_s": 0.233, "tokens": 41501, "tokens_input_uncached": 6854, "tokens_cache_read": 34304, "tokens_cache_write": null, "tokens_output": 343, "tokens_reasoning": 32, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 7430, "tokens_proxy_cache_read": 34304, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 368, "tokens_proxy_reasoning": 32, "tokens_proxy_calls": 6, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7197, "turns": 5, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_fix-failing-test_aab7e1z2"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784453182.4021633, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:fix-failing-test:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T05:26:29", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 37.947, "t_env_setup_s": 0.001, "t_agent_s": 37.947, "t_checker_s": 0.218, "tokens": 86212, "tokens_input_uncached": 12879, "tokens_cache_read": 72704, "tokens_cache_write": null, "tokens_output": 629, "tokens_reasoning": 36, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 13455, "tokens_proxy_cache_read": 72704, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 654, "tokens_proxy_reasoning": 36, "tokens_proxy_calls": 11, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 13508, "turns": 10, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_fix-failing-test_c97gfcyn"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784453212.039549, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:fix-failing-test:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T05:27:07", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 20.651, "t_env_setup_s": 0.001, "t_agent_s": 20.651, "t_checker_s": 0.23, "tokens": 49774, "tokens_input_uncached": 9491, "tokens_cache_read": 39936, "tokens_cache_write": null, "tokens_output": 347, "tokens_reasoning": 22, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 10067, "tokens_proxy_cache_read": 39936, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 372, "tokens_proxy_reasoning": 22, "tokens_proxy_calls": 7, "sampling_observed": [{"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}, {"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 9838, "turns": 6, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_fix-failing-test_4jsfvqby"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784453241.3518317, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:make-ci-green:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T05:27:28", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 58.65, "t_env_setup_s": 0.005, "t_agent_s": 58.65, "t_checker_s": 0.23, "tokens": 128341, "tokens_input_uncached": 18778, "tokens_cache_read": 108032, "tokens_cache_write": null, "tokens_output": 1531, "tokens_reasoning": 75, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 19346, "tokens_proxy_cache_read": 108032, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1556, "tokens_proxy_reasoning": 75, "tokens_proxy_calls": 12, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 20309, "turns": 11, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_make-ci-green_d1bri6d1"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784453289.3381593, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784453289.3382185, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784453289.3382695, "sha256": ""}, "catalog/inventory.py": {"bytes": 744, "mtime": 1784453289.338293, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784453289.3381333, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784453289.338335, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:make-ci-green:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T05:28:27", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 57.178, "t_env_setup_s": 0.006, "t_agent_s": 57.178, "t_checker_s": 0.234, "tokens": 122078, "tokens_input_uncached": 21075, "tokens_cache_read": 99328, "tokens_cache_write": null, "tokens_output": 1675, "tokens_reasoning": 186, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 21643, "tokens_proxy_cache_read": 99328, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1700, "tokens_proxy_reasoning": 186, "tokens_proxy_calls": 11, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 22750, "turns": 10, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_make-ci-green_ihilulsi"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784453342.7829936, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784453342.7832577, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784453342.7830873, "sha256": ""}, "catalog/inventory.py": {"bytes": 744, "mtime": 1784453342.7832189, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784453342.7832804, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784453342.7832901, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:make-ci-green:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T05:29:25", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 66.179, "t_env_setup_s": 0.003, "t_agent_s": 66.179, "t_checker_s": 0.232, "tokens": 155216, "tokens_input_uncached": 25154, "tokens_cache_read": 128000, "tokens_cache_write": null, "tokens_output": 2062, "tokens_reasoning": 310, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 25722, "tokens_proxy_cache_read": 128000, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2087, "tokens_proxy_reasoning": 310, "tokens_proxy_calls": 13, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 27216, "turns": 12, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_make-ci-green_64dbpc9_"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784453400.6253958, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784453400.6253006, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784453400.625244, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784453414.795061, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784453400.6255505, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784453400.6256955, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:make-it-run:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T05:30:31", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 29.084, "t_env_setup_s": 0.002, "t_agent_s": 29.084, "t_checker_s": 0.24, "tokens": 49384, "tokens_input_uncached": 6932, "tokens_cache_read": 41984, "tokens_cache_write": null, "tokens_output": 468, "tokens_reasoning": 54, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 7480, "tokens_proxy_cache_read": 41984, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 491, "tokens_proxy_reasoning": 54, "tokens_proxy_calls": 7, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7400, "turns": 6, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_make-it-run_2v4k6b_v"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784453448.6894033, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784453445.5421555, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:make-it-run:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T05:31:01", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 39.207, "t_env_setup_s": 0.001, "t_agent_s": 39.207, "t_checker_s": 0.237, "tokens": 94835, "tokens_input_uncached": 12543, "tokens_cache_read": 81408, "tokens_cache_write": null, "tokens_output": 884, "tokens_reasoning": 75, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 13091, "tokens_proxy_cache_read": 81408, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 908, "tokens_proxy_reasoning": 75, "tokens_proxy_calls": 12, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 13427, "turns": 11, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_make-it-run_hvzfzlwy"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784453481.7174463, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784453481.717426, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:make-it-run:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T05:31:40", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 20.835, "t_env_setup_s": 0.001, "t_agent_s": 20.835, "t_checker_s": 0.236, "tokens": 40682, "tokens_input_uncached": 8045, "tokens_cache_read": 32256, "tokens_cache_write": null, "tokens_output": 381, "tokens_reasoning": 34, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 8593, "tokens_proxy_cache_read": 32256, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 405, "tokens_proxy_reasoning": 34, "tokens_proxy_calls": 6, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8426, "turns": 5, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_make-it-run_xyhbwpxx"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784453514.8147306, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784453514.8147738, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:misleading-error:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T05:32:01", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 20.461, "t_env_setup_s": 0.003, "t_agent_s": 20.461, "t_checker_s": 0.238, "tokens": 41984, "tokens_input_uncached": 7255, "tokens_cache_read": 34304, "tokens_cache_write": null, "tokens_output": 425, "tokens_reasoning": 68, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 7884, "tokens_proxy_cache_read": 34304, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 449, "tokens_proxy_reasoning": 68, "tokens_proxy_calls": 6, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7680, "turns": 5, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_misleading-error_jj1kvvg5"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784453537.0203874, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:misleading-error:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T05:32:22", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 37.846, "t_env_setup_s": 0.002, "t_agent_s": 37.846, "t_checker_s": 0.237, "tokens": 78230, "tokens_input_uncached": 13502, "tokens_cache_read": 64000, "tokens_cache_write": null, "tokens_output": 728, "tokens_reasoning": 90, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 14131, "tokens_proxy_cache_read": 64000, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 753, "tokens_proxy_reasoning": 90, "tokens_proxy_calls": 10, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 14230, "turns": 9, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_misleading-error_ici0mdk_"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784453566.5611255, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:misleading-error:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T05:33:01", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 48.299, "t_env_setup_s": 0.002, "t_agent_s": 48.299, "t_checker_s": 0.236, "tokens": 97973, "tokens_input_uncached": 11324, "tokens_cache_read": 85504, "tokens_cache_write": null, "tokens_output": 1145, "tokens_reasoning": 245, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 11953, "tokens_proxy_cache_read": 85504, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1169, "tokens_proxy_reasoning": 245, "tokens_proxy_calls": 12, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 12469, "turns": 11, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_misleading-error_2tpjg4sx"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784453605.2228646, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:taskflow:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T05:33:49", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 89.38, "t_env_setup_s": 0.011, "t_agent_s": 89.38, "t_checker_s": 0.227, "tokens": 418102, "tokens_input_uncached": 43099, "tokens_cache_read": 372736, "tokens_cache_write": null, "tokens_output": 2267, "tokens_reasoning": 301, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 43934, "tokens_proxy_cache_read": 372736, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2295, "tokens_proxy_reasoning": 301, "tokens_proxy_calls": 15, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 45366, "turns": 14, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_taskflow_t_tep41i"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, "sha256": ""}, "tests/test_resources.py": {"bytes": 773, "mtime": 1784160991.2269812, "sha256": ""}, "tests/test_retry.py": {"bytes": 833, "mtime": 1784160991.2270508, "sha256": ""}, "tests/test_statemachine.py": {"bytes": 567, "mtime": 1784160991.2271175, "sha256": ""}, "taskflow/__init__.py": {"bytes": 2080, "mtime": 1784160991.2241614, "sha256": ""}, "taskflow/analysis.py": {"bytes": 8183, "mtime": 1784160991.224232, "sha256": ""}, "taskflow/builder.py": {"bytes": 8904, "mtime": 1784160991.2243223, "sha256": ""}, "taskflow/cli.py": {"bytes": 7365, "mtime": 1784160991.2243776, "sha256": ""}, "taskflow/config.py": {"bytes": 6988, "mtime": 1784453687.0563924, "sha256": ""}, "taskflow/dag.py": {"bytes": 15744, "mtime": 1784160991.224575, "sha256": ""}, "taskflow/dag_algorithms.py": {"bytes": 12614, "mtime": 1784160991.2246811, "sha256": ""}, "taskflow/diagnostics.py": {"bytes": 7380, "mtime": 1784160991.224753, "sha256": ""}, "taskflow/events.py": {"bytes": 6312, "mtime": 1784453687.0561798, "sha256": ""}, "taskflow/export.py": {"bytes": 5319, "mtime": 1784160991.2248626, "sha256": ""}, "taskflow/filters.py": {"bytes": 8531, "mtime": 1784160991.2249525, "sha256": ""}, "taskflow/history.py": {"bytes": 8566, "mtime": 1784160991.2250726, "sha256": ""}, "taskflow/hooks.py": {"bytes": 10205, "mtime": 1784160991.2251635, "sha256": ""}, "taskflow/metrics.py": {"bytes": 11622, "mtime": 1784160991.2252398, "sha256": ""}, "taskflow/model.py": {"bytes": 16049, "mtime": 1784160991.2253256, "sha256": ""}, "taskflow/planner.py": {"bytes": 11805, "mtime": 1784160991.225468, "sha256": ""}, "taskflow/policies.py": {"bytes": 10634, "mtime": 1784160991.2255614, "sha256": ""}, "taskflow/queue.py": {"bytes": 5687, "mtime": 1784453687.0560188, "sha256": ""}, "taskflow/reporting.py": {"bytes": 9271, "mtime": 1784160991.2257166, "sha256": ""}, "taskflow/resources.py": {"bytes": 7281, "mtime": 1784453687.0561008, "sha256": ""}, "taskflow/retry.py": {"bytes": 6056, "mtime": 1784453687.0560403, "sha256": ""}, "taskflow/runner.py": {"bytes": 4452, "mtime": 1784160991.2259035, "sha256": ""}, "taskflow/scheduler.py": {"bytes": 14635, "mtime": 1784453687.05645, "sha256": ""}, "taskflow/serialization.py": {"bytes": 9103, "mtime": 1784160991.2260818, "sha256": ""}, "taskflow/simulation.py": {"bytes": 7494, "mtime": 1784160991.226148, "sha256": ""}, "taskflow/statemachine.py": {"bytes": 6500, "mtime": 1784453687.0565493, "sha256": ""}, "taskflow/stats.py": {"bytes": 12341, "mtime": 1784160991.226304, "sha256": ""}, "taskflow/templating.py": {"bytes": 8884, "mtime": 1784160991.2263978, "sha256": ""}, "taskflow/timeutil.py": {"bytes": 9180, "mtime": 1784160991.2264779, "sha256": ""}, "taskflow/validation.py": {"bytes": 12447, "mtime": 1784160991.2265797, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:taskflow:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T05:35:19", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 86.512, "t_env_setup_s": 0.009, "t_agent_s": 86.512, "t_checker_s": 0.222, "tokens": 408814, "tokens_input_uncached": 46508, "tokens_cache_read": 359936, "tokens_cache_write": null, "tokens_output": 2370, "tokens_reasoning": 365, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 47343, "tokens_proxy_cache_read": 359936, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2398, "tokens_proxy_reasoning": 365, "tokens_proxy_calls": 14, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 48878, "turns": 13, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_taskflow_r75m3d98"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, 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true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 50845, "turns": 15, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_taskflow_xzqtduob"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, 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null, "candidate_provenance": null, "version_drift": false} {"run_id": "grokbuild:webcore:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T05:38:12", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "webcore", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 446.876, "t_env_setup_s": 0.011, "t_agent_s": 446.876, "t_checker_s": 0.28, "tokens": 1020643, "tokens_input_uncached": 56755, "tokens_cache_read": 944640, "tokens_cache_write": null, "tokens_output": 19248, "tokens_reasoning": 7329, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 59093, "tokens_proxy_cache_read": 944640, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 19275, "tokens_proxy_reasoning": 7329, "tokens_proxy_calls": 32, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 76003, "turns": 31, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_webcore_thzqxdbc"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 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"grokbuild", "model": "gpt-5.6-sol", "task": "webcore", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 595.056, "t_env_setup_s": 0.011, "t_agent_s": 595.056, "t_checker_s": 0.229, "tokens": 1428688, "tokens_input_uncached": 70650, "tokens_cache_read": 1332736, "tokens_cache_write": null, "tokens_output": 25302, "tokens_reasoning": 10574, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 72988, "tokens_proxy_cache_read": 1332736, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 25329, "tokens_proxy_reasoning": 10574, "tokens_proxy_calls": 42, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 95952, "turns": 41, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_webcore_9cakya6z"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 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"grokbuild", "model": "gpt-5.6-sol", "task": "webcore", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 533.683, "t_env_setup_s": 0.011, "t_agent_s": 533.683, "t_checker_s": 0.282, "tokens": 968132, "tokens_input_uncached": 78054, "tokens_cache_read": 869888, "tokens_cache_write": null, "tokens_output": 20190, "tokens_reasoning": 7808, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 78984, "tokens_proxy_cache_read": 871296, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 20217, "tokens_proxy_reasoning": 7808, "tokens_proxy_calls": 36, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 98244, "turns": 35, "cmd": ["/opt/homebrew/bin/grok", "--no-auto-update", "-p", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/bench_grokbuild_webcore_hw4iamvh"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 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"claude", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 88.313, "t_env_setup_s": 0.012, "t_agent_s": 88.313, "t_checker_s": 0.229, "tokens": 18618, "tokens_input_uncached": 14795, "tokens_cache_read": 18944, "tokens_cache_write": 0, "tokens_output": 3823, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 13658, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 18944, "output_tokens": 3807, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 14795, "outputTokens": 3823, "cacheReadInputTokens": 18944, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.17902199999999996, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 14795, "tokens_proxy_cache_read": 18944, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3823, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 9, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 18618, "turns": 16, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 755, "mtime": 1783083001.4625738, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1853, "mtime": 1784462902.7171226, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:add-feature:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T08:09:12", "harness": "claude", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 95.907, "t_env_setup_s": 0.003, "t_agent_s": 95.907, "t_checker_s": 0.23, "tokens": 19881, "tokens_input_uncached": 15714, "tokens_cache_read": 40448, "tokens_cache_write": 0, "tokens_output": 4167, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 14577, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 40448, "output_tokens": 4151, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 15714, "outputTokens": 4167, "cacheReadInputTokens": 40448, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.202969, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 15714, "tokens_proxy_cache_read": 40448, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 4167, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 12, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 19881, "turns": 21, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3062, "mtime": 1784463016.058582, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1665, "mtime": 1784462983.8747163, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:add-feature:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T08:10:49", "harness": "claude", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 110.264, "t_env_setup_s": 0.003, "t_agent_s": 110.264, "t_checker_s": 0.233, "tokens": 31067, "tokens_input_uncached": 25864, "tokens_cache_read": 39936, "tokens_cache_write": 0, "tokens_output": 5203, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 24727, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 39936, "output_tokens": 5187, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 25864, "outputTokens": 5203, "cacheReadInputTokens": 39936, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.279363, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 25864, "tokens_proxy_cache_read": 39936, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5203, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 12, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 31067, "turns": 25, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3060, "mtime": 1784463127.0198016, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1802, "mtime": 1784463100.046839, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:build-a-cli:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T08:12:39", "harness": "claude", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 22.556, "t_env_setup_s": 0.001, "t_agent_s": 22.556, "t_checker_s": 0.291, "tokens": 7318, "tokens_input_uncached": 6482, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 836, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 5509, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1536, "output_tokens": 822, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 6482, "outputTokens": 836, "cacheReadInputTokens": 1536, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.054078, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 6482, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 836, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 5, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7318, "turns": 5, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1217, "mtime": 1784463175.806303, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:build-a-cli:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T08:13:02", "harness": "claude", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 22.554, "t_env_setup_s": 0.001, "t_agent_s": 22.554, "t_checker_s": 0.284, "tokens": 9236, "tokens_input_uncached": 8241, "tokens_cache_read": 0, "tokens_cache_write": 0, "tokens_output": 995, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 7268, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0, "output_tokens": 982, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 8241, "outputTokens": 995, "cacheReadInputTokens": 0, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.06608, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 8241, "tokens_proxy_cache_read": 0, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 995, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 5, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 9236, "turns": 5, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1351, "mtime": 1784463198.568113, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:build-a-cli:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T08:13:25", "harness": "claude", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 28.612, "t_env_setup_s": 0.0, "t_agent_s": 28.612, "t_checker_s": 0.292, "tokens": 7193, "tokens_input_uncached": 6394, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 799, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 5421, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1536, "output_tokens": 786, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 6394, "outputTokens": 799, "cacheReadInputTokens": 1536, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.052713, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 6394, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 799, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 5, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7193, "turns": 5, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1203, "mtime": 1784463224.5073545, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:fix-failing-test:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T08:13:54", "harness": "claude", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 14.362, "t_env_setup_s": 0.002, "t_agent_s": 14.362, "t_checker_s": 0.232, "tokens": 6038, "tokens_input_uncached": 5646, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 392, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 4897, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1536, "output_tokens": 380, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 5646, "outputTokens": 392, "cacheReadInputTokens": 1536, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.038798000000000006, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 5646, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 392, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 5, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 6038, "turns": 6, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784463242.7935548, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:fix-failing-test:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T08:14:09", "harness": "claude", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 14.066, "t_env_setup_s": 0.001, "t_agent_s": 14.066, "t_checker_s": 0.235, "tokens": 4513, "tokens_input_uncached": 4181, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 332, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 3432, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3072, "output_tokens": 320, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 4181, "outputTokens": 332, "cacheReadInputTokens": 3072, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.030741000000000004, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 4181, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 332, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 5, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 4513, "turns": 5, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784463257.3935888, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:fix-failing-test:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T08:14:23", "harness": "claude", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 13.854, "t_env_setup_s": 0.001, "t_agent_s": 13.854, "t_checker_s": 0.234, "tokens": 4646, "tokens_input_uncached": 4279, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 367, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 3530, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3072, "output_tokens": 355, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 4279, "outputTokens": 367, "cacheReadInputTokens": 3072, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.032105999999999996, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 4279, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 367, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 5, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 4646, "turns": 5, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784463271.8318028, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:make-ci-green:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T08:14:38", "harness": "claude", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 60.304, "t_env_setup_s": 0.005, "t_agent_s": 60.304, "t_checker_s": 0.236, "tokens": 19931, "tokens_input_uncached": 17911, "tokens_cache_read": 51712, "tokens_cache_write": 0, "tokens_output": 2020, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 17170, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 51712, "output_tokens": 2008, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 17911, "outputTokens": 2020, "cacheReadInputTokens": 51712, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.16591099999999998, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 17911, "tokens_proxy_cache_read": 51712, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2020, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 13, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 19931, "turns": 25, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784463312.902477, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784463322.6710086, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784463319.1926198, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784463330.0206292, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784463315.8796802, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784463326.0993483, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:make-ci-green:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T08:15:38", "harness": "claude", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 27.59, "t_env_setup_s": 0.004, "t_agent_s": 27.59, "t_checker_s": 0.235, "tokens": 10556, "tokens_input_uncached": 9516, "tokens_cache_read": 12800, "tokens_cache_write": 0, "tokens_output": 1040, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 8775, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 12800, "output_tokens": 1028, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 9516, "outputTokens": 1040, "cacheReadInputTokens": 12800, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.07998, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 9516, "tokens_proxy_cache_read": 12800, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1040, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 6, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 10556, "turns": 11, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784463359.0164194, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784463359.0231388, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784463359.0295997, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784463359.03538, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784463359.0553555, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784463359.058858, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:make-ci-green:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T08:16:06", "harness": "claude", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 57.282, "t_env_setup_s": 0.003, "t_agent_s": 57.282, "t_checker_s": 0.232, "tokens": 22833, "tokens_input_uncached": 20872, "tokens_cache_read": 48640, "tokens_cache_write": 0, "tokens_output": 1961, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 20131, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 48640, "output_tokens": 1949, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 20872, "outputTokens": 1961, "cacheReadInputTokens": 48640, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.17770500000000003, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 20872, "tokens_proxy_cache_read": 48640, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1961, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 13, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 22833, "turns": 25, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784463397.2127182, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784463402.778413, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784463406.1216729, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784463411.843842, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784463414.7537293, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784463418.7105696, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:make-it-run:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T08:17:04", "harness": "claude", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 20.918, "t_env_setup_s": 0.002, "t_agent_s": 20.918, "t_checker_s": 0.237, "tokens": 8126, "tokens_input_uncached": 7495, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 631, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 6774, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3072, "output_tokens": 619, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 7495, "outputTokens": 631, "cacheReadInputTokens": 3072, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.054785999999999994, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 7495, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 631, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 7, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8126, "turns": 9, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784463436.7418704, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784463436.7365415, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:make-it-run:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T08:17:25", "harness": "claude", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 20.069, "t_env_setup_s": 0.001, "t_agent_s": 20.069, "t_checker_s": 0.235, "tokens": 7849, "tokens_input_uncached": 7182, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 667, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 6461, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3072, "output_tokens": 655, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 7182, "outputTokens": 667, "cacheReadInputTokens": 3072, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.05412100000000001, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 7182, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 667, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 7, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7849, "turns": 8, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784463457.6357207, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784463457.6304526, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:make-it-run:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T08:17:46", "harness": "claude", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 23.932, "t_env_setup_s": 0.001, "t_agent_s": 23.932, "t_checker_s": 0.227, "tokens": 9910, "tokens_input_uncached": 9301, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 609, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 8580, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3072, "output_tokens": 597, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 9301, "outputTokens": 609, "cacheReadInputTokens": 3072, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.063266, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 9301, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 609, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 8, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 9910, "turns": 9, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784463479.6130853, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784463476.4902465, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:misleading-error:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T08:18:10", "harness": "claude", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 16.729, "t_env_setup_s": 0.003, "t_agent_s": 16.729, "t_checker_s": 0.237, "tokens": 8275, "tokens_input_uncached": 7691, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 584, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 6889, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3072, "output_tokens": 572, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 7691, "outputTokens": 584, "cacheReadInputTokens": 3072, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.054590999999999994, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 7691, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 584, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 6, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8275, "turns": 10, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784463503.5644844, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:misleading-error:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T08:18:27", "harness": "claude", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 21.545, "t_env_setup_s": 0.002, "t_agent_s": 21.545, "t_checker_s": 0.235, "tokens": 9609, "tokens_input_uncached": 8872, "tokens_cache_read": 4608, "tokens_cache_write": 0, "tokens_output": 737, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 8070, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 4608, "output_tokens": 725, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 8872, "outputTokens": 737, "cacheReadInputTokens": 4608, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.065089, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 8872, "tokens_proxy_cache_read": 4608, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 737, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 7, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 9609, "turns": 11, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784463520.9603746, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:misleading-error:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T08:18:49", "harness": "claude", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 23.67, "t_env_setup_s": 0.002, "t_agent_s": 23.67, "t_checker_s": 0.234, "tokens": 7980, "tokens_input_uncached": 7359, "tokens_cache_read": 3072, "tokens_cache_write": 0, "tokens_output": 621, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 6557, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3072, "output_tokens": 609, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 7359, "outputTokens": 621, "cacheReadInputTokens": 3072, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.053856, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 7359, "tokens_proxy_cache_read": 3072, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 621, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 6, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 7980, "turns": 10, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784463548.144116, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:taskflow:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T08:19:13", "harness": "claude", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 60.843, "t_env_setup_s": 0.013, "t_agent_s": 60.843, "t_checker_s": 0.228, "tokens": 48341, "tokens_input_uncached": 46137, "tokens_cache_read": 143360, "tokens_cache_write": 0, "tokens_output": 2204, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 45129, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 143360, "output_tokens": 2191, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 46137, "outputTokens": 2204, "cacheReadInputTokens": 143360, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.35746500000000003, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 46137, "tokens_proxy_cache_read": 143360, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2204, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 9, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 48341, "turns": 29, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 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"claude", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 56.774, "t_env_setup_s": 0.01, "t_agent_s": 56.774, "t_checker_s": 0.233, "tokens": 48802, "tokens_input_uncached": 46539, "tokens_cache_read": 179712, "tokens_cache_write": 0, "tokens_output": 2263, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 45531, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 179712, "output_tokens": 2250, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 46539, "outputTokens": 2263, "cacheReadInputTokens": 179712, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.37912599999999996, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 46539, "tokens_proxy_cache_read": 179712, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2263, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 10, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 48802, "turns": 30, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 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"claude", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 75.599, "t_env_setup_s": 0.01, "t_agent_s": 75.599, "t_checker_s": 0.228, "tokens": 52643, "tokens_input_uncached": 50354, "tokens_cache_read": 398848, "tokens_cache_write": 0, "tokens_output": 2289, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 49346, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 398848, "output_tokens": 2276, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 50354, "outputTokens": 2289, "cacheReadInputTokens": 398848, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.508419, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 50354, "tokens_proxy_cache_read": 398848, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2289, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 16, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 52643, "turns": 31, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 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"claude", "model": "gpt-5.6-sol", "task": "webcore", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 366.802, "t_env_setup_s": 0.012, "t_agent_s": 366.802, "t_checker_s": 0.242, "tokens": 100000, "tokens_input_uncached": 82335, "tokens_cache_read": 474624, "tokens_cache_write": 0, "tokens_output": 17665, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 79824, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 474624, "output_tokens": 17652, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 82335, "outputTokens": 17665, "cacheReadInputTokens": 474624, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 1.0906119999999997, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 82335, "tokens_proxy_cache_read": 474624, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 17665, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 22, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 100000, "turns": 34, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, "webcore/__init__.py": {"bytes": 2152, "mtime": 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32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 88158, "turns": 37, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "2.1.214 (Claude Code) (/.local/bin/claude)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, "webcore/__init__.py": {"bytes": 2136, "mtime": 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"model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 69005, "turns": 31, "cmd": ["/.local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). 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When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: recovered rows do not match the checker-owned oracle\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "recovered.json": {"bytes": 408, "mtime": 1784464954.7958536, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T08:42:37", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 681.563, "t_env_setup_s": 0.094, "t_agent_s": 681.409, "t_checker_s": 0.232, "tokens": 21395, "tokens_input_uncached": 19167, "tokens_cache_read": 49152, "tokens_cache_write": 0, "tokens_output": 2228, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 18328, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 49152, "output_tokens": 2214, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 19167, "outputTokens": 2228, "cacheReadInputTokens": 49152, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.176111, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 19167, "tokens_proxy_cache_read": 49152, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 2228, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 15, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 21395, "turns": 18, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_051b373a", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_db-wal-recovery_nh4axrn4:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_hiiz56zo.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=MtQ0ATI3-0LTOsNkGvkhBA28", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: recovered rows do not match the checker-owned oracle\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784465627.5141406, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T08:53:59", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 38.934, "t_env_setup_s": 0.096, "t_agent_s": 38.784, "t_checker_s": 0.231, "tokens": 9572, "tokens_input_uncached": 8542, "tokens_cache_read": 1536, "tokens_cache_write": 0, "tokens_output": 1030, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 7703, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1536, "output_tokens": 1016, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 8542, "outputTokens": 1030, "cacheReadInputTokens": 1536, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.069228, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 8542, "tokens_proxy_cache_read": 1536, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1030, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 6, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 9572, "turns": 7, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_263a4382", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_db-wal-recovery_056tlj0j:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_jyq1lyrj.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=4r5xMt_jXgq0VLDUC4mVm14Y", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784465676.079505, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784465667.361843, "sha256": ""}, "main.db-wal.encrypted.bak": {"bytes": 16512, "mtime": 1784465667.3533247, "sha256": ""}, "recovered.json": {"bytes": 472, "mtime": 1784465676.0799553, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/extract-elf:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T08:54:39", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 1, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 99.947, "t_env_setup_s": 0.087, "t_agent_s": 99.796, "t_checker_s": 0.232, "tokens": 13829, "tokens_input_uncached": 12208, "tokens_cache_read": 10752, "tokens_cache_write": 0, "tokens_output": 1621, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 11397, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 10752, "output_tokens": 1608, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 12208, "outputTokens": 1621, "cacheReadInputTokens": 10752, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.10694100000000001, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 12208, "tokens_proxy_cache_read": 10752, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1621, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 8, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 13829, "turns": 14, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_6627872b", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_extract-elf_m9rjk695:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_8_f_35kp.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=a6Sk9iwPN-RhtYZhWL6Z5RJu", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: extract.js does not exist\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/extract-elf:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T08:56:19", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 100.378, "t_env_setup_s": 0.088, "t_agent_s": 100.227, "t_checker_s": 0.288, "tokens": 27815, "tokens_input_uncached": 23317, "tokens_cache_read": 45056, "tokens_cache_write": 0, "tokens_output": 4498, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 22506, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 45056, "output_tokens": 4485, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 23317, "outputTokens": 4498, "cacheReadInputTokens": 45056, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.251563, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 23317, "tokens_proxy_cache_read": 45056, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 4498, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 11, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 27815, "turns": 14, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_e86a16fb", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_extract-elf_99witi8m:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_hqe5lhni.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=QaCSoebkETUUxhUBBM0jWl3y", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 4009, "mtime": 1784465864.8250897, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/extract-elf:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T08:58:00", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 105.894, "t_env_setup_s": 0.085, "t_agent_s": 105.756, "t_checker_s": 0.279, "tokens": 27880, "tokens_input_uncached": 22764, "tokens_cache_read": 18432, "tokens_cache_write": 0, "tokens_output": 5116, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 21953, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 18432, "output_tokens": 5103, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 22764, "outputTokens": 5116, "cacheReadInputTokens": 18432, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.25093600000000005, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 22764, "tokens_proxy_cache_read": 18432, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5116, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 9, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 27880, "turns": 14, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_84ba78cb", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_extract-elf_3gfa5rfg:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_e5he2kbl.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=eL04L1s1L-ghZkOGOgIphUKn", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 4595, "mtime": 1784465980.7043493, "sha256": ""}, "out.json": {"bytes": 9959, "mtime": 1784465980.7235308, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T08:59:46", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 190.733, "t_env_setup_s": 0.082, "t_agent_s": 190.588, "t_checker_s": 0.585, "tokens": 13748, "tokens_input_uncached": 9957, "tokens_cache_read": 16896, "tokens_cache_write": 0, "tokens_output": 3791, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 8420, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 11264, "output_tokens": 3527, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 9957, "outputTokens": 3791, "cacheReadInputTokens": 16896, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.153008, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 9957, "tokens_proxy_cache_read": 16896, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3791, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 9, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 13748, "turns": 7, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_d64b2fd8", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_feal-differential-cryptanalysis_0wfheinf:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ldahxtto.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=m6akY-wTBOiKH969Xb2iWj_b", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 2899, "mtime": 1784466074.954056, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T09:02:57", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 2, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 184.021, "t_env_setup_s": 0.085, "t_agent_s": 183.875, "t_checker_s": 0.236, "tokens": 8438, "tokens_input_uncached": 6488, "tokens_cache_read": 7680, "tokens_cache_write": 0, "tokens_output": 1950, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 5388, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 5120, "output_tokens": 1761, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 6488, "outputTokens": 1950, "cacheReadInputTokens": 7680, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.08503, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 6488, "tokens_proxy_cache_read": 7680, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1950, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 8, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 8438, "turns": 7, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_a3db38a0", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_feal-differential-cryptanalysis_cymaflok:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_63o8e3wk.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=E7evYmoE5Aca_YRMAf2LNLxJ", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T09:06:02", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 129.401, "t_env_setup_s": 0.094, "t_agent_s": 129.25, "t_checker_s": 0.535, "tokens": 12409, "tokens_input_uncached": 9207, "tokens_cache_read": 8704, "tokens_cache_write": 0, "tokens_output": 3202, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 8378, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 8704, "output_tokens": 3187, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 9207, "outputTokens": 3202, "cacheReadInputTokens": 8704, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.130437, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 9207, "tokens_proxy_cache_read": 8704, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 3202, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 6, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 12409, "turns": 6, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_d91d7c6f", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_feal-differential-cryptanalysis_vxsgrgk_:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_5ou389tw.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=UQHbJI6LBb6JReKfJznneeVK", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 2525, "mtime": 1784466478.5070958, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/gcode-to-text:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T09:08:12", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 386.639, "t_env_setup_s": 0.093, "t_agent_s": 386.484, "t_checker_s": 0.23, "tokens": 107487, "tokens_input_uncached": 97942, "tokens_cache_read": 303104, "tokens_cache_write": 0, "tokens_output": 9545, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 97234, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 303104, "output_tokens": 9532, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 97942, "outputTokens": 9545, "cacheReadInputTokens": 303104, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.8798870000000001, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 97942, "tokens_proxy_cache_read": 303104, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 9545, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 34, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}, {"model": "gpt-5.6-sol"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 107487, "turns": 48, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_95537bb8", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_gcode-to-text_s6f4fktq:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_dfv02nqg.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=1XgHqjiXPzCIUwrJPRgU_MW9", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: out.txt does not contain the decoded printed text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784466871.7132018, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/gcode-to-text:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T09:14:39", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 168.247, "t_env_setup_s": 0.094, "t_agent_s": 168.094, "t_checker_s": 0.245, "tokens": 56681, "tokens_input_uncached": 49996, "tokens_cache_read": 205824, "tokens_cache_write": 0, "tokens_output": 6685, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 49288, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 205824, "output_tokens": 6672, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 49996, "outputTokens": 6685, "cacheReadInputTokens": 205824, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.5200170000000002, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 49996, "tokens_proxy_cache_read": 205824, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6685, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 22, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 56681, "turns": 23, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_593e93bc", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_gcode-to-text_281f3_7a:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_29tk9erq.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=0XA_H7MPoAE0qqup-qaAw47o", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784467043.7345016, "sha256": ""}, "pca12.png": {"bytes": 4540, "mtime": 1784467020.1939623, "sha256": ""}, "pca12.svg": {"bytes": 1935551, "mtime": 1784466970.9947946, "sha256": ""}, "pca12_h.png": {"bytes": 4547, "mtime": 1784467020.2132087, "sha256": ""}, "pca12_hv.png": {"bytes": 4568, "mtime": 1784467020.2504077, "sha256": ""}, "pca12_v.png": {"bytes": 4582, "mtime": 1784467020.2317464, "sha256": ""}, "pca13.png": {"bytes": 1199, "mtime": 1784467020.4868293, "sha256": ""}, "pca13.svg": {"bytes": 1926967, "mtime": 1784466971.1639373, "sha256": ""}, "pca13_h.png": {"bytes": 1223, "mtime": 1784467020.4897776, "sha256": ""}, "pca13_hv.png": {"bytes": 1190, "mtime": 1784467020.495789, "sha256": ""}, "pca13_v.png": {"bytes": 1189, "mtime": 1784467020.4926825, "sha256": ""}, "pca23.png": {"bytes": 166, "mtime": 1784467020.7489855, "sha256": ""}, "pca23.svg": {"bytes": 1837201, "mtime": 1784466971.323231, "sha256": ""}, "pca23_h.png": {"bytes": 167, "mtime": 1784467020.7494617, "sha256": ""}, "pca23_hv.png": {"bytes": 165, "mtime": 1784467020.750339, "sha256": ""}, "pca23_v.png": {"bytes": 165, "mtime": 1784467020.749877, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/gcode-to-text:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T09:17:28", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 260.723, "t_env_setup_s": 0.085, "t_agent_s": 260.592, "t_checker_s": 0.245, "tokens": 39191, "tokens_input_uncached": 33609, "tokens_cache_read": 121856, "tokens_cache_write": 0, "tokens_output": 5582, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 32901, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 121856, "output_tokens": 5569, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 33609, "outputTokens": 5582, "cacheReadInputTokens": 121856, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.368523, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 33609, "tokens_proxy_cache_read": 121856, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5582, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 25, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}, {"model": "gpt-5.6-sol"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 39191, "turns": 25, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_50f51a47", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_gcode-to-text_ul3ycmy4:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_smblfmnh.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=z2nbkWhZNm7RGqXJEOVc-pKk", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"hflip.png": {"bytes": 1956, "mtime": 1784467266.7171693, "sha256": ""}, "out.txt": {"bytes": 27, "mtime": 1784467302.4250715, "sha256": ""}, "proj0.bmp": {"bytes": 180054, "mtime": 1784467236.3031418, "sha256": ""}, "proj0.png": {"bytes": 1979, "mtime": 1784467244.754769, "sha256": ""}, "proj0.ppm": {"bytes": 180015, "mtime": 1784467149.9624524, "sha256": ""}, "proj1.bmp": {"bytes": 180054, "mtime": 1784467236.3174417, "sha256": ""}, "proj1.png": {"bytes": 720, "mtime": 1784467244.7574422, "sha256": ""}, "proj1.ppm": {"bytes": 180015, "mtime": 1784467150.12301, "sha256": ""}, "proj2.bmp": {"bytes": 705654, "mtime": 1784467236.3550792, "sha256": ""}, "proj2.png": {"bytes": 2288, "mtime": 1784467244.7697763, "sha256": ""}, "proj2.ppm": {"bytes": 705615, "mtime": 1784467151.1217544, "sha256": ""}, "rot180.png": {"bytes": 1974, "mtime": 1784467266.7369838, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}, "vflip.png": {"bytes": 2000, "mtime": 1784467266.725928, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T09:21:49", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 115.509, "t_env_setup_s": 0.087, "t_agent_s": 115.37, "t_checker_s": 0.23, "tokens": 27830, "tokens_input_uncached": 23272, "tokens_cache_read": 68608, "tokens_cache_write": 0, "tokens_output": 4558, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 21799, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 68608, "output_tokens": 4545, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 23272, "outputTokens": 4558, "cacheReadInputTokens": 68608, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.264614, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 23272, "tokens_proxy_cache_read": 68608, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 4558, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 11, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 27830, "turns": 12, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_753fc89e", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_llm-inference-batching-scheduler_ocuzok4b:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr__e54wjc2.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=AB4lJY1S2ZSRVpyxkpY05eg9", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784467417.9615624, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784467417.964741, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T09:23:45", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 516.522, "t_env_setup_s": 0.085, "t_agent_s": 516.378, "t_checker_s": 0.23, "tokens": 29017, "tokens_input_uncached": 21782, "tokens_cache_read": 91648, "tokens_cache_write": 0, "tokens_output": 7235, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 20309, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 91648, "output_tokens": 7222, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 21782, "outputTokens": 7235, "cacheReadInputTokens": 91648, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.335609, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 21782, "tokens_proxy_cache_read": 91648, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 7235, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 12, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 29017, "turns": 14, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_169bd165", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_llm-inference-batching-scheduler_g4y04930:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_sifo8rmc.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=HdouQWNlF7iMGhat-6p217m_", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88800, "mtime": 1784467917.3060606, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784467917.3127472, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T09:32:22", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 94.636, "t_env_setup_s": 0.095, "t_agent_s": 94.504, "t_checker_s": 0.231, "tokens": 21971, "tokens_input_uncached": 17724, "tokens_cache_read": 39936, "tokens_cache_write": 0, "tokens_output": 4247, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 16251, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 39936, "output_tokens": 4232, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 17724, "outputTokens": 4247, "cacheReadInputTokens": 39936, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.214763, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 17724, "tokens_proxy_cache_read": 39936, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 4247, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 8, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 21971, "turns": 10, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_dfb13431", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_llm-inference-batching-scheduler_wh1dfqcl:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_yzix9_hc.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=AcyrZAmLHrJuiB_xTfKfdIoi", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784468015.1106532, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 88800, "mtime": 1784468015.1143792, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/raman-fitting:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T09:33:57", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 131.704, "t_env_setup_s": 0.089, "t_agent_s": 131.553, "t_checker_s": 0.232, "tokens": 28589, "tokens_input_uncached": 23316, "tokens_cache_read": 62464, "tokens_cache_write": 0, "tokens_output": 5273, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 22494, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 62464, "output_tokens": 5261, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 23316, "outputTokens": 5273, "cacheReadInputTokens": 62464, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.279637, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 23316, "tokens_proxy_cache_read": 62464, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5273, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 16, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 28589, "turns": 15, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_eb0d175d", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_raman-fitting_c_k_pjcg:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_lb3yhoou.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=Unx_lsLLBuddE2IORsr1nSQw", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 239, "mtime": 1784468165.2107642, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/raman-fitting:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T09:36:09", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 165.58, "t_env_setup_s": 0.094, "t_agent_s": 165.425, "t_checker_s": 0.234, "tokens": 73087, "tokens_input_uncached": 66742, "tokens_cache_read": 249856, "tokens_cache_write": 0, "tokens_output": 6345, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 65920, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 249856, "output_tokens": 6333, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 66742, "outputTokens": 6345, "cacheReadInputTokens": 249856, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.617263, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 66742, "tokens_proxy_cache_read": 249856, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6345, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 15, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}, {"model": "gpt-5.6-sol"}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 73087, "turns": 16, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_8cb81849", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_raman-fitting_wd9ds832:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_3zn6ixzv.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=UY6nYvg6grupARJ2E5RCwPWv", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 233, "mtime": 1784468329.2667434, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/raman-fitting:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T09:38:55", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 119.371, "t_env_setup_s": 0.085, "t_agent_s": 119.231, "t_checker_s": 0.238, "tokens": 25686, "tokens_input_uncached": 20313, "tokens_cache_read": 37888, "tokens_cache_write": 0, "tokens_output": 5373, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 19491, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 37888, "output_tokens": 5361, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 20313, "outputTokens": 5373, "cacheReadInputTokens": 37888, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.254834, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 20313, "tokens_proxy_cache_read": 37888, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5373, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 13, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 25686, "turns": 12, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_b53636c6", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_raman-fitting_u6myxn7c:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_2deiie20.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=29xpAkBjPuskIrqasNLiBz1r", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 223, "mtime": 1784468452.3395793, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "claude:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T09:40:54", "harness": "claude", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 507.477, "t_env_setup_s": 0.096, "t_agent_s": 507.347, "t_checker_s": 25.989, "tokens": 60959, "tokens_input_uncached": 49401, "tokens_cache_read": 496128, "tokens_cache_write": 0, "tokens_output": 11558, "tokens_reasoning": null, "usage_raw": {"usage": {"input_tokens": 48334, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 496128, "output_tokens": 11544, "server_tool_use": {"web_search_requests": 0, "web_fetch_requests": 0}, "service_tier": "standard", "cache_creation": {"ephemeral_1h_input_tokens": 0, "ephemeral_5m_input_tokens": 0}, "inference_geo": "", "iterations": [], "speed": "standard"}, "modelUsage": {"gpt-5.6-sol": {"inputTokens": 49401, "outputTokens": 11558, "cacheReadInputTokens": 496128, "cacheCreationInputTokens": 0, "webSearchRequests": 0, "costUSD": 0.7840190000000001, "contextWindow": 200000, "maxOutputTokens": 32000}}}, "token_basis": "vendor_split", "tokens_proxy_input_uncached": 49401, "tokens_proxy_cache_read": 496128, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 11558, "tokens_proxy_reasoning": null, "tokens_proxy_calls": 29, "sampling_observed": [{"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true, "thinking": {"display": "omitted", "type": "adaptive"}}, {"max_tokens": 32000, "model": "gpt-5.6-sol", "stream": true}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 60959, "turns": 31, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_claude_82169_96d001aa", "-v", "/dev/openbench/.bench-tmp/bench_claude_terminal-bench_schemelike-metacircular-eval_qezbvbfg:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_jdkdqlsj.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:64682", "-e", "OPENBENCH_PROXY_CELL_TOKEN=fpOySsRAcraQh_YFyo6I_Wvm", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "claude", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/claude", "-p", "--bare", "--output-format", "json", "--model", "gpt-5.6-sol", "--effort", "medium", "--dangerously-skip-permissions", "--disallowedTools", "Agent", "Task", "--no-session-persistence", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784468897.2815726, "sha256": ""}, "eval.scm": {"bytes": 10479, "mtime": 1784468848.521437, "sha256": ""}, "handler-test.txt": {"bytes": 23, "mtime": 1784468946.8988187, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, 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{"bytes": 2565, "mtime": 1784160991.2210133, "sha256": ""}, "shadow_test/recursive_structures.scm": {"bytes": 2994, "mtime": 1784160991.221053, "sha256": ""}, "shadow_test/test_read.scm": {"bytes": 90, "mtime": 1784160991.22109, "sha256": ""}, "shadow_test/variadic_functions.scm": {"bytes": 2434, "mtime": 1784160991.221138, "sha256": ""}, "shadow_test/y_combinator.scm": {"bytes": 877, "mtime": 1784160991.2211754, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:add-feature:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:20:02", "harness": "cursor", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 57.16, "t_env_setup_s": 0.092, "t_agent_s": 57.001, "t_checker_s": 0.266, "tokens": 2653, "tokens_input_uncached": 27, "tokens_cache_read": 149028, "tokens_cache_write": 21116, "tokens_output": 2626, "tokens_reasoning": null, "usage_raw": {"inputTokens": 27, "outputTokens": 2626, "cacheReadTokens": 149028, "cacheWriteTokens": 21116}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 2653, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_387256ee", "-v", "/dev/openbench/.bench-tmp/bench_cursor_add-feature_gi_0rzmi:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_6mzijl_m.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3201, "mtime": 1784470849.7524924, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1607, "mtime": 1784470823.8227446, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:add-feature:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:21:00", "harness": "cursor", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 47.85, "t_env_setup_s": 0.086, "t_agent_s": 47.705, "t_checker_s": 0.228, "tokens": 2538, "tokens_input_uncached": 27, "tokens_cache_read": 148412, "tokens_cache_write": 20699, "tokens_output": 2511, "tokens_reasoning": null, "usage_raw": {"inputTokens": 27, "outputTokens": 2511, "cacheReadTokens": 148412, "cacheWriteTokens": 20699}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 2538, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_4be948b0", "-v", "/dev/openbench/.bench-tmp/bench_cursor_add-feature_g3yf2u5q:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_lrplr1aq.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3134, "mtime": 1784470895.3273199, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1663, "mtime": 1784470883.3200455, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:add-feature:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:21:48", "harness": "cursor", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 55.282, "t_env_setup_s": 0.083, "t_agent_s": 55.137, "t_checker_s": 0.225, "tokens": 2871, "tokens_input_uncached": 33, "tokens_cache_read": 188919, "tokens_cache_write": 21553, "tokens_output": 2838, "tokens_reasoning": null, "usage_raw": {"inputTokens": 33, "outputTokens": 2838, "cacheReadTokens": 188919, "cacheWriteTokens": 21553}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 2871, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_52050292", "-v", "/dev/openbench/.bench-tmp/bench_cursor_add-feature_gvpg3tty:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_rhqqjji0.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 2609, "mtime": 1784470955.1337106, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1648, "mtime": 1784470932.2320569, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:build-a-cli:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:22:43", "harness": "cursor", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 26.593, "t_env_setup_s": 0.084, "t_agent_s": 26.461, "t_checker_s": 0.285, "tokens": 1314, "tokens_input_uncached": 15, "tokens_cache_read": 66598, "tokens_cache_write": 17885, "tokens_output": 1299, "tokens_reasoning": null, "usage_raw": {"inputTokens": 15, "outputTokens": 1299, "cacheReadTokens": 66598, "cacheWriteTokens": 17885}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1314, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_4e6509b8", "-v", "/dev/openbench/.bench-tmp/bench_cursor_build-a-cli_4txfi84k:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_em9wjwya.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1872, "mtime": 1784470980.516708, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:build-a-cli:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:23:10", "harness": "cursor", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 29.925, "t_env_setup_s": 0.085, "t_agent_s": 29.776, "t_checker_s": 0.298, "tokens": 1213, "tokens_input_uncached": 15, "tokens_cache_read": 67006, "tokens_cache_write": 17563, "tokens_output": 1198, "tokens_reasoning": null, "usage_raw": {"inputTokens": 15, "outputTokens": 1198, "cacheReadTokens": 67006, "cacheWriteTokens": 17563}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1213, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_024cfcca", "-v", "/dev/openbench/.bench-tmp/bench_cursor_build-a-cli_bdbpvnle:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_4ij98gw_.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1416, "mtime": 1784471007.8570344, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:build-a-cli:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:23:41", "harness": "cursor", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 31.005, "t_env_setup_s": 0.094, "t_agent_s": 30.862, "t_checker_s": 0.227, "tokens": 1305, "tokens_input_uncached": 15, "tokens_cache_read": 66111, "tokens_cache_write": 17618, "tokens_output": 1290, "tokens_reasoning": null, "usage_raw": {"inputTokens": 15, "outputTokens": 1290, "cacheReadTokens": 66111, "cacheWriteTokens": 17618}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1305, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_3c45c971", "-v", "/dev/openbench/.bench-tmp/bench_cursor_build-a-cli_254clhf3:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_yr_tz6_1.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1411, "mtime": 1784471041.089043, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:fix-failing-test:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:24:12", "harness": "cursor", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 12.512, "t_env_setup_s": 0.085, "t_agent_s": 12.383, "t_checker_s": 0.226, "tokens": 427, "tokens_input_uncached": 15, "tokens_cache_read": 65856, "tokens_cache_write": 16911, "tokens_output": 412, "tokens_reasoning": null, "usage_raw": {"inputTokens": 15, "outputTokens": 412, "cacheReadTokens": 65856, "cacheWriteTokens": 16911}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 427, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_8d599426", "-v", "/dev/openbench/.bench-tmp/bench_cursor_fix-failing-test_w4gptxyd:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_mwnz3sud.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784471062.0181615, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:fix-failing-test:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:24:25", "harness": "cursor", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 12.367, "t_env_setup_s": 0.089, "t_agent_s": 12.225, "t_checker_s": 0.236, "tokens": 434, "tokens_input_uncached": 15, "tokens_cache_read": 65867, "tokens_cache_write": 16918, "tokens_output": 419, "tokens_reasoning": null, "usage_raw": {"inputTokens": 15, "outputTokens": 419, "cacheReadTokens": 65867, "cacheWriteTokens": 16918}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 434, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_f90cfb7a", "-v", "/dev/openbench/.bench-tmp/bench_cursor_fix-failing-test_4q61sjh1:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_pji0j4fu.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784471074.423532, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:fix-failing-test:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:24:37", "harness": "cursor", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 15.158, "t_env_setup_s": 0.087, "t_agent_s": 15.014, "t_checker_s": 0.236, "tokens": 446, "tokens_input_uncached": 15, "tokens_cache_read": 65771, "tokens_cache_write": 16895, "tokens_output": 431, "tokens_reasoning": null, "usage_raw": {"inputTokens": 15, "outputTokens": 431, "cacheReadTokens": 65771, "cacheWriteTokens": 16895}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 446, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_f264d1f1", "-v", "/dev/openbench/.bench-tmp/bench_cursor_fix-failing-test_u5k03py5:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_7rwafjcn.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784471089.7932167, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:make-ci-green:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:24:53", "harness": "cursor", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 38.971, "t_env_setup_s": 0.094, "t_agent_s": 38.824, "t_checker_s": 0.242, "tokens": 1522, "tokens_input_uncached": 33, "tokens_cache_read": 196313, "tokens_cache_write": 21348, "tokens_output": 1489, "tokens_reasoning": null, "usage_raw": {"inputTokens": 33, "outputTokens": 1489, "cacheReadTokens": 196313, "cacheWriteTokens": 21348}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1522, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_67829038", "-v", "/dev/openbench/.bench-tmp/bench_cursor_make-ci-green_yodwrqsz:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_2llkfj3v.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784471112.3357315, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784471114.6829958, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784471116.5261154, "sha256": ""}, "catalog/inventory.py": {"bytes": 761, "mtime": 1784471119.385088, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784471121.0429103, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784471124.5098658, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:make-ci-green:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:25:32", "harness": "cursor", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 30.304, "t_env_setup_s": 0.077, "t_agent_s": 30.184, "t_checker_s": 0.232, "tokens": 1131, "tokens_input_uncached": 30, "tokens_cache_read": 156737, "tokens_cache_write": 41899, "tokens_output": 1101, "tokens_reasoning": null, "usage_raw": {"inputTokens": 30, "outputTokens": 1101, "cacheReadTokens": 156737, "cacheWriteTokens": 41899}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1131, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_e751761f", "-v", "/dev/openbench/.bench-tmp/bench_cursor_make-ci-green_cnbsooca:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_myjezvze.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784471145.1072795, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784471149.2788215, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784471151.2337213, "sha256": ""}, "catalog/inventory.py": {"bytes": 744, "mtime": 1784471152.9935794, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784471154.644194, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784471156.9223738, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:make-ci-green:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:26:02", "harness": "cursor", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 33.952, "t_env_setup_s": 0.083, "t_agent_s": 33.819, "t_checker_s": 0.168, "tokens": 1335, "tokens_input_uncached": 33, "tokens_cache_read": 195588, "tokens_cache_write": 21239, "tokens_output": 1302, "tokens_reasoning": null, "usage_raw": {"inputTokens": 33, "outputTokens": 1302, "cacheReadTokens": 195588, "cacheWriteTokens": 21239}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1335, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_c260ec33", "-v", "/dev/openbench/.bench-tmp/bench_cursor_make-ci-green_dpnatye4:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_vxrm0ujm.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784471180.7103264, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784471183.3615398, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784471184.9368773, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784471186.5070496, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784471188.9299693, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784471190.7110944, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:make-it-run:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:26:36", "harness": "cursor", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 16.875, "t_env_setup_s": 0.086, "t_agent_s": 16.746, "t_checker_s": 0.171, "tokens": 521, "tokens_input_uncached": 18, "tokens_cache_read": 81687, "tokens_cache_write": 16742, "tokens_output": 503, "tokens_reasoning": null, "usage_raw": {"inputTokens": 18, "outputTokens": 503, "cacheReadTokens": 81687, "cacheWriteTokens": 16742}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 521, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_82439646", "-v", "/dev/openbench/.bench-tmp/bench_cursor_make-it-run_j99xfa_a:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_flse7z5p.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784471209.9530618, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784471208.4978604, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:make-it-run:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:26:53", "harness": "cursor", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 26.247, "t_env_setup_s": 0.095, "t_agent_s": 26.105, "t_checker_s": 0.173, "tokens": 534, "tokens_input_uncached": 18, "tokens_cache_read": 81706, "tokens_cache_write": 16748, "tokens_output": 516, "tokens_reasoning": null, "usage_raw": {"inputTokens": 18, "outputTokens": 516, "cacheReadTokens": 81706, "cacheWriteTokens": 16748}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 534, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_b0eb379c", "-v", "/dev/openbench/.bench-tmp/bench_cursor_make-it-run_xbjtsjsi:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_tm7eq714.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784471228.0366433, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784471226.5073988, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:make-it-run:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:27:20", "harness": "cursor", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 15.895, "t_env_setup_s": 0.088, "t_agent_s": 15.749, "t_checker_s": 0.173, "tokens": 560, "tokens_input_uncached": 18, "tokens_cache_read": 81880, "tokens_cache_write": 16790, "tokens_output": 542, "tokens_reasoning": null, "usage_raw": {"inputTokens": 18, "outputTokens": 542, "cacheReadTokens": 81880, "cacheWriteTokens": 16790}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 560, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_a008fb84", "-v", "/dev/openbench/.bench-tmp/bench_cursor_make-it-run_mfxmx73h:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_jt6n9k5r.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784471252.5675325, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784471251.278401, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:misleading-error:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:27:36", "harness": "cursor", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 16.038, "t_env_setup_s": 0.087, "t_agent_s": 15.897, "t_checker_s": 0.171, "tokens": 539, "tokens_input_uncached": 15, "tokens_cache_read": 66109, "tokens_cache_write": 17074, "tokens_output": 524, "tokens_reasoning": null, "usage_raw": {"inputTokens": 15, "outputTokens": 524, "cacheReadTokens": 66109, "cacheWriteTokens": 17074}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 539, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_c20ba203", "-v", "/dev/openbench/.bench-tmp/bench_cursor_misleading-error_pq0xlc_3:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ih264abk.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784471267.4603395, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:misleading-error:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:27:52", "harness": "cursor", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 16.683, "t_env_setup_s": 0.089, "t_agent_s": 16.536, "t_checker_s": 0.171, "tokens": 541, "tokens_input_uncached": 21, "tokens_cache_read": 99792, "tokens_cache_write": 17107, "tokens_output": 520, "tokens_reasoning": null, "usage_raw": {"inputTokens": 21, "outputTokens": 520, "cacheReadTokens": 99792, "cacheWriteTokens": 17107}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 541, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_5712f827", "-v", "/dev/openbench/.bench-tmp/bench_cursor_misleading-error_pwojx4l1:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_y6vj2kxh.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784471285.943466, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:misleading-error:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:28:09", "harness": "cursor", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 19.517, "t_env_setup_s": 0.091, "t_agent_s": 19.361, "t_checker_s": 0.17, "tokens": 615, "tokens_input_uncached": 21, "tokens_cache_read": 99814, "tokens_cache_write": 17167, "tokens_output": 594, "tokens_reasoning": null, "usage_raw": {"inputTokens": 21, "outputTokens": 594, "cacheReadTokens": 99814, "cacheWriteTokens": 17167}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 615, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_53aab701", "-v", "/dev/openbench/.bench-tmp/bench_cursor_misleading-error__u71gv0y:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_2v9fsrai.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784471305.5962217, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:taskflow:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:28:29", "harness": "cursor", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 53.398, "t_env_setup_s": 0.096, "t_agent_s": 53.249, "t_checker_s": 0.234, "tokens": 1559, "tokens_input_uncached": 42, "tokens_cache_read": 549751, "tokens_cache_write": 55463, "tokens_output": 1517, "tokens_reasoning": null, "usage_raw": {"inputTokens": 42, "outputTokens": 1517, "cacheReadTokens": 549751, "cacheWriteTokens": 55463}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1559, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_3624b0f4", "-v", "/dev/openbench/.bench-tmp/bench_cursor_taskflow_jk7o7kf7:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_zvzqhxj3.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, "sha256": ""}, "tests/test_resources.py": {"bytes": 773, "mtime": 1784160991.2269812, "sha256": ""}, "tests/test_retry.py": {"bytes": 833, "mtime": 1784160991.2270508, "sha256": ""}, "tests/test_statemachine.py": {"bytes": 567, "mtime": 1784160991.2271175, "sha256": ""}, "taskflow/__init__.py": {"bytes": 2080, "mtime": 1784160991.2241614, "sha256": ""}, "taskflow/analysis.py": {"bytes": 8183, "mtime": 1784160991.224232, "sha256": ""}, "taskflow/builder.py": {"bytes": 8904, "mtime": 1784160991.2243223, "sha256": ""}, "taskflow/cli.py": {"bytes": 7365, "mtime": 1784160991.2243776, "sha256": ""}, "taskflow/config.py": {"bytes": 6988, "mtime": 1784471339.4630227, "sha256": ""}, "taskflow/dag.py": {"bytes": 15744, "mtime": 1784160991.224575, "sha256": ""}, "taskflow/dag_algorithms.py": {"bytes": 12614, "mtime": 1784160991.2246811, "sha256": ""}, "taskflow/diagnostics.py": {"bytes": 7380, "mtime": 1784160991.224753, "sha256": ""}, "taskflow/events.py": {"bytes": 6312, "mtime": 1784471348.5212283, "sha256": ""}, "taskflow/export.py": {"bytes": 5319, "mtime": 1784160991.2248626, "sha256": ""}, "taskflow/filters.py": {"bytes": 8531, "mtime": 1784160991.2249525, "sha256": ""}, "taskflow/history.py": {"bytes": 8566, "mtime": 1784160991.2250726, "sha256": ""}, "taskflow/hooks.py": {"bytes": 10205, "mtime": 1784160991.2251635, "sha256": ""}, "taskflow/metrics.py": {"bytes": 11622, "mtime": 1784160991.2252398, "sha256": ""}, "taskflow/model.py": {"bytes": 16049, "mtime": 1784160991.2253256, "sha256": ""}, "taskflow/planner.py": {"bytes": 11805, "mtime": 1784160991.225468, "sha256": ""}, "taskflow/policies.py": {"bytes": 10634, "mtime": 1784160991.2255614, "sha256": ""}, "taskflow/queue.py": {"bytes": 5687, "mtime": 1784471341.663879, "sha256": ""}, "taskflow/reporting.py": {"bytes": 9271, "mtime": 1784160991.2257166, "sha256": ""}, "taskflow/resources.py": {"bytes": 7281, "mtime": 1784471347.08052, "sha256": ""}, "taskflow/retry.py": {"bytes": 6056, "mtime": 1784471343.4307158, "sha256": ""}, "taskflow/runner.py": {"bytes": 4452, "mtime": 1784160991.2259035, "sha256": ""}, "taskflow/scheduler.py": {"bytes": 14635, "mtime": 1784471354.8571234, "sha256": ""}, "taskflow/serialization.py": {"bytes": 9103, "mtime": 1784160991.2260818, "sha256": ""}, "taskflow/simulation.py": {"bytes": 7494, "mtime": 1784160991.226148, "sha256": ""}, "taskflow/statemachine.py": {"bytes": 6500, "mtime": 1784471345.0872073, "sha256": ""}, "taskflow/stats.py": {"bytes": 12341, "mtime": 1784160991.226304, "sha256": ""}, "taskflow/templating.py": {"bytes": 8884, "mtime": 1784160991.2263978, "sha256": ""}, "taskflow/timeutil.py": {"bytes": 9180, "mtime": 1784160991.2264779, "sha256": ""}, "taskflow/validation.py": {"bytes": 12447, "mtime": 1784160991.2265797, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:taskflow:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:29:22", "harness": "cursor", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 32.178, "t_env_setup_s": 0.096, "t_agent_s": 32.046, "t_checker_s": 0.229, "tokens": 1433, "tokens_input_uncached": 36, "tokens_cache_read": 459974, "tokens_cache_write": 110857, "tokens_output": 1397, "tokens_reasoning": null, "usage_raw": {"inputTokens": 36, "outputTokens": 1397, "cacheReadTokens": 459974, "cacheWriteTokens": 110857}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1433, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_07c239b7", "-v", "/dev/openbench/.bench-tmp/bench_cursor_taskflow_0dwfr6ds:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_dvnqhmiw.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, "sha256": ""}, 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"gpt-5.6-sol", "task": "taskflow", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 37.177, "t_env_setup_s": 0.093, "t_agent_s": 37.034, "t_checker_s": 0.219, "tokens": 1515, "tokens_input_uncached": 39, "tokens_cache_read": 582614, "tokens_cache_write": 56027, "tokens_output": 1476, "tokens_reasoning": null, "usage_raw": {"inputTokens": 39, "outputTokens": 1476, "cacheReadTokens": 582614, "cacheWriteTokens": 56027}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 1515, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_d386f7b1", "-v", "/dev/openbench/.bench-tmp/bench_cursor_taskflow_mmpfqwmw:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_xakvduus.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, "sha256": ""}, 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"gpt-5.6-sol", "task": "webcore", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 152.825, "t_env_setup_s": 0.095, "t_agent_s": 152.685, "t_checker_s": 0.233, "tokens": 6796, "tokens_input_uncached": 24, "tokens_cache_read": 179729, "tokens_cache_write": 67781, "tokens_output": 6772, "tokens_reasoning": null, "usage_raw": {"inputTokens": 24, "outputTokens": 6772, "cacheReadTokens": 179729, "cacheWriteTokens": 67781}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 6796, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_1341_6213625f", "-v", "/dev/openbench/.bench-tmp/bench_cursor_webcore_rb9hosyb:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_8ht0ntf8.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.3, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL(feat) test_feature_converters.ConverterFeatureTest.test_int_converter_matches_digits_as_int\nFAIL(feat) test_feature_converters.ConverterFeatureTest.test_slug_converter_accepts_and_rejects\nFAIL(feat) test_feature_converters.ConverterFeatureTest.test_path_converter_matches_multiple_segments\nFAIL(feat) test_feature_fallthrough.FallthroughFeatureTest.test_int_route_wins_for_digits\nFAIL(feat) test_feature_fallthrough.FallthroughFeatureTest.test_non_int_falls_through_to_slug\nFAIL(feat) test_feature_fallthrough.FallthroughFeatureTest.test_no_candidate_is_404\nFAIL(feat) test_feature_precedence.PrecedenceFeatureTest.test_static_beats_dynamic_regardless_of_order\nFAIL(feat) test_feature_precedence.PrecedenceFeatureTest.test_path_converter_is_lowest_precedence\nFAIL(feat) test_feature_paramtypes.ParamTypeFeatureTest.test_int_param_arrives_as_int\nFAIL(feat) test_feature_paramtypes.ParamTypeFeatureTest.test_path_param_contains_slashes\nFAIL(feat) test_feature_mount.MountFeatureTest.test_parametric_prefix_and_route_params_merged\nFAIL(feat) test_feature_mount.MountFeatureTest.test_static_prefix_mount\nFAIL(feat) test_feature_mountmiddleware.MountMiddlewareFeatureTest.test_parent_then_child_then_handler\nFAIL(feat) test_feature_mountmiddleware.MountMiddlewareFeatureTest.test_parent_middleware_short_circuits_child\nFAIL(feat) test_feature_trailing.TrailingSlashFeatureTest.test_extra_slash_redirects_to_registered\nFAIL(feat) test_feature_trailing.TrailingSlashFeatureTest.test_query_string_preserved_on_redirect\nFAIL(feat) test_feature_trailing.TrailingSlashFeatureTest.test_registered_with_slash_redirects_missing_slash\nFAIL(feat) test_feature_method.MethodNotAllowedFeatureTest.test_wrong_method_is_405\nFAIL(feat) test_feature_method.MethodNotAllowedFeatureTest.test_allow_header_is_sorted\nFAIL(feat) test_feature_urlfor.UrlForFeatureTest.test_int_formatting_and_query_string\nFAIL(feat) test_feature_urlfor.UrlForFeatureTest.test_mount_prefix_included\nFAIL(feat) test_feature_urlfor.UrlForFeatureTest.test_missing_param_raises\nFAIL(feat) test_feature_headoptions.HeadOptionsFeatureTest.test_head_has_headers_but_empty_body\nFAIL(feat) test_feature_headoptions.HeadOptionsFeatureTest.test_options_returns_204_with_allow\nregression 19/19 feature 0/24\nSCORE: 0.3000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, "webcore/__init__.py": {"bytes": 2009, "mtime": 1784160991.2289624, "sha256": ""}, "webcore/app.py": {"bytes": 3746, "mtime": 1784160991.229023, "sha256": ""}, "webcore/blueprints.py": {"bytes": 6421, "mtime": 1784160991.2290688, "sha256": ""}, "webcore/caching.py": {"bytes": 8842, "mtime": 1784160991.2291315, "sha256": ""}, "webcore/compression.py": {"bytes": 4113, "mtime": 1784160991.2291734, "sha256": ""}, "webcore/config.py": {"bytes": 5996, "mtime": 1784160991.2292168, "sha256": ""}, "webcore/converters.py": {"bytes": 1103, "mtime": 1784160991.2292557, "sha256": ""}, "webcore/cookies.py": {"bytes": 5889, "mtime": 1784160991.2293212, 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"--cpus", "4", "--name", "openbench_cursor_1341_e340a162", "-v", "/dev/openbench/.bench-tmp/bench_cursor_webcore_2yfpi4w5:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_co6hhejm.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 5190, "mtime": 1784471816.1948376, "sha256": ""}, "webcore/__init__.py": {"bytes": 2152, "mtime": 1784471785.1013215, "sha256": ""}, 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"HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4903, "mtime": 1784472197.506688, "sha256": ""}, "webcore/__init__.py": {"bytes": 2166, "mtime": 1784472195.8899484, "sha256": ""}, 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"tokens_input_uncached": 30, "tokens_cache_read": 157766, "tokens_cache_write": 19737, "tokens_output": 2365, "tokens_reasoning": null, "usage_raw": {"inputTokens": 30, "outputTokens": 2365, "cacheReadTokens": 157766, "cacheWriteTokens": 19737}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 2395, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_dc2dba76", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_db-wal-recovery_1w9ge35f:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ppahy1qt.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784472241.9509532, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784472232.465893, "sha256": ""}, "main.db-wal.encoded-backup": {"bytes": 16512, "mtime": 1784160991.207109, "sha256": ""}, "recovered.json": {"bytes": 408, "mtime": 1784472241.9516096, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:44:08", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 45.226, "t_env_setup_s": 0.09, "t_agent_s": 45.072, "t_checker_s": 0.236, "tokens": 2191, "tokens_input_uncached": 24, "tokens_cache_read": 119422, "tokens_cache_write": 19581, "tokens_output": 2167, "tokens_reasoning": null, "usage_raw": {"inputTokens": 24, "outputTokens": 2167, "cacheReadTokens": 119422, "cacheWriteTokens": 19581}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 2191, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_e6bad6a5", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_db-wal-recovery_jqj9l347:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_o1engdet.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784472291.2202098, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784472285.7926548, "sha256": ""}, "recovered.json": {"bytes": 472, "mtime": 1784472285.796564, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:44:53", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 43.259, "t_env_setup_s": 0.083, "t_agent_s": 43.124, "t_checker_s": 0.218, "tokens": 2264, "tokens_input_uncached": 33, "tokens_cache_read": 174307, "tokens_cache_write": 19536, "tokens_output": 2231, "tokens_reasoning": null, "usage_raw": {"inputTokens": 33, "outputTokens": 2231, "cacheReadTokens": 174307, "cacheWriteTokens": 19536}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 2264, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_586e0b80", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_db-wal-recovery_yx3qlnqy:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_oldkemkb.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784472331.3085005, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784472324.0737717, "sha256": ""}, "recovered.json": {"bytes": 408, "mtime": 1784472331.3091762, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/extract-elf:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:45:36", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 112.922, "t_env_setup_s": 0.089, "t_agent_s": 112.771, "t_checker_s": 0.297, "tokens": 6087, "tokens_input_uncached": 33, "tokens_cache_read": 199994, "tokens_cache_write": 27312, "tokens_output": 6054, "tokens_reasoning": null, "usage_raw": {"inputTokens": 33, "outputTokens": 6054, "cacheReadTokens": 199994, "cacheWriteTokens": 27312}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 6087, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_63af08bb", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_extract-elf_e2ty1ivg:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ge1r8vgu.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194872', '4194876', '4194880', '4194884', '4194888']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 5156, "mtime": 1784472437.2225032, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/extract-elf:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:47:30", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 96.375, "t_env_setup_s": 0.089, "t_agent_s": 96.237, "t_checker_s": 0.283, "tokens": 5413, "tokens_input_uncached": 27, "tokens_cache_read": 153590, "tokens_cache_write": 25414, "tokens_output": 5386, "tokens_reasoning": null, "usage_raw": {"inputTokens": 27, "outputTokens": 5386, "cacheReadTokens": 153590, "cacheWriteTokens": 25414}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 5413, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_1a396fc6", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_extract-elf_jjdht7yy:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_j7vtx60x.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 4683, "mtime": 1784472528.8392043, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/extract-elf:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:49:06", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 101.362, "t_env_setup_s": 0.086, "t_agent_s": 101.212, "t_checker_s": 0.285, "tokens": 5662, "tokens_input_uncached": 30, "tokens_cache_read": 175280, "tokens_cache_write": 25648, "tokens_output": 5632, "tokens_reasoning": null, "usage_raw": {"inputTokens": 30, "outputTokens": 5632, "cacheReadTokens": 175280, "cacheWriteTokens": 25648}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 5662, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_09dc5bf7", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_extract-elf_onwwj693:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_g97fpnt4.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 5243, "mtime": 1784472616.2721796, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:50:48", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 1, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 25.182, "t_env_setup_s": 0.087, "t_agent_s": 25.05, "t_checker_s": 0.243, "tokens": null, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": null, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_398e05c4", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_feal-differential-cryptanalysis_75maqw89:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_iygyiuje.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "infra", "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:51:13", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 105.411, "t_env_setup_s": 0.09, "t_agent_s": 105.262, "t_checker_s": 0.525, "tokens": 3691, "tokens_input_uncached": 21, "tokens_cache_read": 110517, "tokens_cache_write": 21116, "tokens_output": 3670, "tokens_reasoning": null, "usage_raw": {"inputTokens": 21, "outputTokens": 3670, "cacheReadTokens": 110517, "cacheWriteTokens": 21116}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 3691, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_f879c869", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_feal-differential-cryptanalysis_7p0it5d2:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_d1ex284u.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 2537, "mtime": 1784472752.2413716, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:52:59", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 3, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 27.646, "t_env_setup_s": 0.084, "t_agent_s": 27.512, "t_checker_s": 0.174, "tokens": null, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": null, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_37695c45", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_feal-differential-cryptanalysis_3gzini_4:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_rthqy_vf.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "infra", "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/gcode-to-text:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:53:27", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 101.068, "t_env_setup_s": 0.095, "t_agent_s": 100.93, "t_checker_s": 0.228, "tokens": 5440, "tokens_input_uncached": 615, "tokens_cache_read": 327186, "tokens_cache_write": 66254, "tokens_output": 4825, "tokens_reasoning": null, "usage_raw": {"inputTokens": 615, "outputTokens": 4825, "cacheReadTokens": 327186, "cacheWriteTokens": 66254}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 5440, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_0d6a36bd", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_gcode-to-text_0zj8ix2d:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_z_r_u6yj.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784472902.8918679, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/gcode-to-text:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T10:55:09", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 104.367, "t_env_setup_s": 0.091, "t_agent_s": 104.229, "t_checker_s": 0.238, "tokens": 5254, "tokens_input_uncached": 74, "tokens_cache_read": 252497, "tokens_cache_write": 67570, "tokens_output": 5180, "tokens_reasoning": null, "usage_raw": {"inputTokens": 74, "outputTokens": 5180, "cacheReadTokens": 252497, "cacheWriteTokens": 67570}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 5254, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_2f328620", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_gcode-to-text_ywbqddk4:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ee_wbuaw.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784473008.9130068, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/gcode-to-text:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T10:56:53", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 106.878, "t_env_setup_s": 0.086, "t_agent_s": 106.731, "t_checker_s": 0.237, "tokens": 5777, "tokens_input_uncached": 621, "tokens_cache_read": 390484, "tokens_cache_write": 69631, "tokens_output": 5156, "tokens_reasoning": null, "usage_raw": {"inputTokens": 621, "outputTokens": 5156, "cacheReadTokens": 390484, "cacheWriteTokens": 69631}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 5777, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_545d90ce", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_gcode-to-text__s9xjzdz:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_88fbqquj.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784473116.6213303, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T10:58:40", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 153.551, "t_env_setup_s": 0.089, "t_agent_s": 153.408, "t_checker_s": 0.233, "tokens": 9043, "tokens_input_uncached": 39, "tokens_cache_read": 311376, "tokens_cache_write": 32995, "tokens_output": 9004, "tokens_reasoning": null, "usage_raw": {"inputTokens": 39, "outputTokens": 9004, "cacheReadTokens": 311376, "cacheWriteTokens": 32995}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 9043, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_ee96109f", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_llm-inference-batching-scheduler_lx131hht:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_mi75l50y.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/optimize_plans.py": {"bytes": 9278, "mtime": 1784473229.4122965, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784473255.0202708, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 88800, "mtime": 1784473254.914343, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T11:01:14", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 110.847, "t_env_setup_s": 0.087, "t_agent_s": 110.699, "t_checker_s": 0.234, "tokens": 6941, "tokens_input_uncached": 30, "tokens_cache_read": 612571, "tokens_cache_write": 84566, "tokens_output": 6911, "tokens_reasoning": null, "usage_raw": {"inputTokens": 30, "outputTokens": 6911, "cacheReadTokens": 612571, "cacheWriteTokens": 84566}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 6941, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_fbe1c986", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_llm-inference-batching-scheduler_vfey8tgz:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_pjp_5d6t.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/shape_aware_packer.py": {"bytes": 6998, "mtime": 1784473373.8688805, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784473376.215509, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784473376.223666, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T11:03:05", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 89.884, "t_env_setup_s": 0.088, "t_agent_s": 89.736, "t_checker_s": 0.235, "tokens": 4818, "tokens_input_uncached": 30, "tokens_cache_read": 612984, "tokens_cache_write": 80854, "tokens_output": 4788, "tokens_reasoning": null, "usage_raw": {"inputTokens": 30, "outputTokens": 4788, "cacheReadTokens": 612984, "cacheWriteTokens": 80854}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 4818, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_648800d2", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_llm-inference-batching-scheduler_mpb_5ow5:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_flzdh607.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784473460.354836, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 88800, "mtime": 1784473460.3593578, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/raman-fitting:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T11:04:35", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 114.47, "t_env_setup_s": 0.08, "t_agent_s": 114.326, "t_checker_s": 0.239, "tokens": 4665, "tokens_input_uncached": 39, "tokens_cache_read": 254538, "tokens_cache_write": 26724, "tokens_output": 4626, "tokens_reasoning": null, "usage_raw": {"inputTokens": 39, "outputTokens": 4626, "cacheReadTokens": 254538, "cacheWriteTokens": 26724}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 4665, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_e49e3187", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_raman-fitting_rexaxtyj:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_3nhwzsmy.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: results.json matches checker-owned Raman fit parameters\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 308, "mtime": 1784473578.6098979, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/raman-fitting:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T11:06:30", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 126.806, "t_env_setup_s": 0.095, "t_agent_s": 126.648, "t_checker_s": 0.165, "tokens": 6230, "tokens_input_uncached": 30, "tokens_cache_read": 630894, "tokens_cache_write": 83942, "tokens_output": 6200, "tokens_reasoning": null, "usage_raw": {"inputTokens": 30, "outputTokens": 6200, "cacheReadTokens": 630894, "cacheWriteTokens": 83942}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 6230, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_059c59d5", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_raman-fitting_no83ntsu:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_91egys8v.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: 2D parameters outside tolerance: offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 236, "mtime": 1784473713.7724767, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/raman-fitting:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T11:08:37", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 183.879, "t_env_setup_s": 0.088, "t_agent_s": 183.733, "t_checker_s": 0.165, "tokens": 9152, "tokens_input_uncached": 42, "tokens_cache_read": 928566, "tokens_cache_write": 82137, "tokens_output": 9110, "tokens_reasoning": null, "usage_raw": {"inputTokens": 42, "outputTokens": 9110, "cacheReadTokens": 928566, "cacheWriteTokens": 82137}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 9152, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_89289e89", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_raman-fitting_6ydn4n_c:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_hzns6tdv.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 253, "mtime": 1784473894.7469473, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "cursor:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T11:11:41", "harness": "cursor", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 842.378, "t_env_setup_s": 0.092, "t_agent_s": 842.238, "t_checker_s": 15.731, "tokens": 16360, "tokens_input_uncached": 108, "tokens_cache_read": 1642071, "tokens_cache_write": 58484, "tokens_output": 16252, "tokens_reasoning": null, "usage_raw": {"inputTokens": 108, "outputTokens": 16252, "cacheReadTokens": 1642071, "cacheWriteTokens": 58484}, "token_basis": "harness_reported", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 16360, "turns": null, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_cursor_5730_c8ab97ba", "-v", "/dev/openbench/.bench-tmp/bench_cursor_terminal-bench_schemelike-metacircular-eval_wunhs6o0:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_xxggbsjl.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-v", "/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784474718.747659, "sha256": ""}, "eval.scm": {"bytes": 12064, "mtime": 1784474681.932344, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 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"/.openbench/cursor-container-auth/.config/cursor/auth.json:/bench/auth/.config/cursor/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "cursor", "gpt-5.6-sol", "2400"], "adapter_cmd": ["cursor-agent", "-p", "--force", "--trust", "--model", "gpt-5.6-sol-medium", "--output-format", "json", "--workspace", "/work", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. 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through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"eval.scm": {"bytes": 13559, "mtime": 1784475579.833266, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, "sha256": ""}, "test/06-interactive-io.scm": 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Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784477286.9711277, "sha256": ""}, "eval.scm": {"bytes": 23596, "mtime": 1784477151.964977, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 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"tokens_reasoning": 529, "usage_raw": [{"total": 6634, "input": 6454, "output": 180, "reasoning": 0, "cache": {"write": 0, "read": 0}}, {"total": 7683, "input": 1698, "output": 353, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 9412, "input": 2412, "output": 153, "reasoning": 191, "cache": {"write": 0, "read": 6656}}, {"total": 10833, "input": 830, "output": 1299, "reasoning": 0, "cache": {"write": 0, "read": 8704}}, {"total": 11048, "input": 2234, "output": 90, "reasoning": 20, "cache": {"write": 0, "read": 8704}}, {"total": 11430, "input": 418, "output": 260, "reasoning": 0, "cache": {"write": 0, "read": 10752}}, {"total": 11731, "input": 720, "output": 251, "reasoning": 8, "cache": {"write": 0, "read": 10752}}, {"total": 12502, "input": 1351, "output": 89, "reasoning": 310, "cache": {"write": 0, "read": 10752}}, {"total": 12721, "input": 847, "output": 98, "reasoning": 0, "cache": {"write": 0, "read": 11776}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 20266, "turns": 9, "cmd": ["opencode", "run", "--dir", "/bench_opencode_add-feature_8ugytapt", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 2968, "mtime": 1784478121.6399062, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1587, "mtime": 1784478121.6380978, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:add-feature:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:22:31", "harness": "opencode", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 84.197, "t_env_setup_s": 0.003, "t_agent_s": 84.197, "t_checker_s": 0.228, "tokens": 21579, "tokens_input_uncached": 17830, "tokens_cache_read": 64512, "tokens_cache_write": 0, "tokens_output": 3749, "tokens_reasoning": 751, "usage_raw": [{"total": 6634, "input": 823, "output": 179, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7704, "input": 1713, "output": 359, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 10677, "input": 2439, "output": 1363, "reasoning": 219, "cache": {"write": 0, "read": 6656}}, {"total": 11061, "input": 2080, "output": 258, "reasoning": 19, "cache": {"write": 0, "read": 8704}}, {"total": 11354, "input": 1375, "output": 243, "reasoning": 8, "cache": {"write": 0, "read": 9728}}, {"total": 12474, "input": 6259, "output": 295, "reasoning": 288, "cache": {"write": 0, "read": 5632}}, {"total": 12748, "input": 1783, "output": 213, "reasoning": 0, "cache": {"write": 0, "read": 10752}}, {"total": 13439, "input": 1358, "output": 88, "reasoning": 217, "cache": {"write": 0, "read": 11776}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 21579, "turns": 8, "cmd": ["opencode", "run", "--dir", "/bench_opencode_add-feature_n2d4rbwn", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3048, "mtime": 1784478223.095784, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1455, "mtime": 1784478196.1173813, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:add-feature:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T12:23:55", "harness": "opencode", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 120.972, "t_env_setup_s": 0.003, "t_agent_s": 120.972, "t_checker_s": 0.226, "tokens": 30695, "tokens_input_uncached": 25574, "tokens_cache_read": 126464, "tokens_cache_write": 0, "tokens_output": 5121, "tokens_reasoning": 1559, "usage_raw": [{"total": 6582, "input": 6456, "output": 126, "reasoning": 0, "cache": {"write": 0, "read": 0}}, {"total": 6819, "input": 1070, "output": 117, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 8463, "input": 2413, "output": 418, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 10451, "input": 2258, "output": 160, "reasoning": 353, "cache": {"write": 0, "read": 7680}}, {"total": 12036, "input": 843, "output": 1465, "reasoning": 0, "cache": {"write": 0, "read": 9728}}, {"total": 12314, "input": 2417, "output": 88, "reasoning": 81, "cache": {"write": 0, "read": 9728}}, {"total": 12632, "input": 658, "output": 198, "reasoning": 0, "cache": {"write": 0, "read": 11776}}, {"total": 14312, "input": 2144, "output": 300, "reasoning": 92, "cache": {"write": 0, "read": 11776}}, {"total": 16659, "input": 2323, "output": 295, "reasoning": 217, "cache": {"write": 0, "read": 13824}}, {"total": 17146, "input": 849, "output": 208, "reasoning": 217, "cache": {"write": 0, "read": 15872}}, {"total": 19763, "input": 3205, "output": 87, "reasoning": 599, "cache": {"write": 0, "read": 15872}}, {"total": 19982, "input": 938, "output": 100, "reasoning": 0, "cache": {"write": 0, "read": 18944}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 30695, "turns": 12, "cmd": ["opencode", "run", "--dir", "/bench_opencode_add-feature_62jx_6f8", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3375, "mtime": 1784478326.78951, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1460, "mtime": 1784478297.9547524, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:build-a-cli:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T12:25:56", "harness": "opencode", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 26.336, "t_env_setup_s": 0.001, "t_agent_s": 26.336, "t_checker_s": 0.227, "tokens": 5814, "tokens_input_uncached": 4865, "tokens_cache_read": 29184, "tokens_cache_write": 0, "tokens_output": 949, "tokens_reasoning": 151, "usage_raw": [{"total": 6395, "input": 661, "output": 81, "reasoning": 21, "cache": {"write": 0, "read": 5632}}, {"total": 6557, "input": 865, "output": 60, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7044, "input": 1024, "output": 352, "reasoning": 36, "cache": {"write": 0, "read": 5632}}, {"total": 7424, "input": 1475, "output": 223, "reasoning": 94, "cache": {"write": 0, "read": 5632}}, {"total": 7578, "input": 840, "output": 82, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 5814, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_build-a-cli_53g0k6jp", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 797, "mtime": 1784478373.5126414, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:build-a-cli:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:26:23", "harness": "opencode", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 25.303, "t_env_setup_s": 0.001, "t_agent_s": 25.303, "t_checker_s": 0.227, "tokens": 5899, "tokens_input_uncached": 4897, "tokens_cache_read": 29184, "tokens_cache_write": 0, "tokens_output": 1002, "tokens_reasoning": 128, "usage_raw": [{"total": 6368, "input": 659, "output": 77, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6527, "input": 836, "output": 59, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7084, "input": 993, "output": 416, "reasoning": 43, "cache": {"write": 0, "read": 5632}}, {"total": 7480, "input": 1513, "output": 250, "reasoning": 85, "cache": {"write": 0, "read": 5632}}, {"total": 7624, "input": 896, "output": 72, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 5899, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_build-a-cli_j00a0vx_", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 1059, "mtime": 1784478399.28019, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:build-a-cli:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T12:26:48", "harness": "opencode", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 27.71, "t_env_setup_s": 0.001, "t_agent_s": 27.71, "t_checker_s": 0.229, "tokens": 4267, "tokens_input_uncached": 3490, "tokens_cache_read": 23552, "tokens_cache_write": 0, "tokens_output": 777, "tokens_reasoning": 95, "usage_raw": [{"total": 6469, "input": 660, "output": 162, "reasoning": 15, "cache": {"write": 0, "read": 5632}}, {"total": 6829, "input": 961, "output": 236, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7219, "input": 1259, "output": 248, "reasoning": 80, "cache": {"write": 0, "read": 5632}}, {"total": 7302, "input": 610, "output": 36, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 4267, "turns": 4, "cmd": ["opencode", "run", "--dir", "/bench_opencode_build-a-cli_gf90q97f", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 440, "mtime": 1784478426.2136517, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:fix-failing-test:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T12:27:16", "harness": "opencode", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 18.575, "t_env_setup_s": 0.001, "t_agent_s": 18.575, "t_checker_s": 0.23, "tokens": 4413, "tokens_input_uncached": 3881, "tokens_cache_read": 30208, "tokens_cache_write": 0, "tokens_output": 532, "tokens_reasoning": 0, "usage_raw": [{"total": 6220, "input": 436, "output": 152, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6606, "input": 841, "output": 133, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7155, "input": 1386, "output": 137, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7287, "input": 560, "output": 71, "reasoning": 0, "cache": {"write": 0, "read": 6656}}, {"total": 7353, "input": 658, "output": 39, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 4413, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_fix-failing-test_unf30hi8", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784478449.7799933, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:fix-failing-test:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:27:35", "harness": "opencode", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 18.217, "t_env_setup_s": 0.002, "t_agent_s": 18.217, "t_checker_s": 0.23, "tokens": 11118, "tokens_input_uncached": 10579, "tokens_cache_read": 23552, "tokens_cache_write": 0, "tokens_output": 539, "tokens_reasoning": 0, "usage_raw": [{"total": 6223, "input": 437, "output": 154, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6614, "input": 847, "output": 135, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7166, "input": 1396, "output": 138, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7300, "input": 7228, "output": 72, "reasoning": 0, "cache": {"write": 0, "read": 0}}, {"total": 7367, "input": 671, "output": 40, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 11118, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_fix-failing-test_trj7rld5", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784478469.6042705, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:fix-failing-test:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T12:27:54", "harness": "opencode", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 17.45, "t_env_setup_s": 0.002, "t_agent_s": 17.45, "t_checker_s": 0.229, "tokens": 11043, "tokens_input_uncached": 10511, "tokens_cache_read": 23552, "tokens_cache_write": 0, "tokens_output": 532, "tokens_reasoning": 0, "usage_raw": [{"total": 6220, "input": 435, "output": 153, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6601, "input": 6470, "output": 131, "reasoning": 0, "cache": {"write": 0, "read": 0}}, {"total": 7149, "input": 1379, "output": 138, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7279, "input": 1577, "output": 70, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7346, "input": 650, "output": 40, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 11043, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_fix-failing-test_mcnxxryw", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784478487.3653018, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:make-ci-green:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T12:28:11", "harness": "opencode", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 60.911, "t_env_setup_s": 0.005, "t_agent_s": 60.911, "t_checker_s": 0.23, "tokens": 28107, "tokens_input_uncached": 25979, "tokens_cache_read": 74240, "tokens_cache_write": 0, "tokens_output": 2128, "tokens_reasoning": 104, "usage_raw": [{"total": 6178, "input": 6058, "output": 120, "reasoning": 0, "cache": {"write": 0, "read": 0}}, {"total": 6491, "input": 662, "output": 197, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 8919, "input": 3194, "output": 84, "reasoning": 9, "cache": {"write": 0, "read": 5632}}, {"total": 9768, "input": 331, "output": 733, "reasoning": 0, "cache": {"write": 0, "read": 8704}}, {"total": 13721, "input": 13046, "output": 580, "reasoning": 95, "cache": {"write": 0, "read": 0}}, {"total": 14076, "input": 1194, "output": 82, "reasoning": 0, "cache": {"write": 0, "read": 12800}}, {"total": 14261, "input": 368, "output": 69, "reasoning": 0, "cache": {"write": 0, "read": 13824}}, {"total": 14370, "input": 465, "output": 81, "reasoning": 0, "cache": {"write": 0, "read": 13824}}, {"total": 14563, "input": 661, "output": 78, "reasoning": 0, "cache": {"write": 0, "read": 13824}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 28107, "turns": 9, "cmd": ["opencode", "run", "--dir", "/bench_opencode_make-ci-green_sv62orhy", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784478539.1652827, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784478539.1668139, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784478539.167183, "sha256": ""}, "catalog/inventory.py": {"bytes": 761, "mtime": 1784478539.1675205, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784478539.1678007, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784478539.16806, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:make-ci-green:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:29:12", "harness": "opencode", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 54.464, "t_env_setup_s": 0.004, "t_agent_s": 54.464, "t_checker_s": 0.229, "tokens": 21437, "tokens_input_uncached": 19381, "tokens_cache_read": 104960, "tokens_cache_write": 0, "tokens_output": 2056, "tokens_reasoning": 21, "usage_raw": [{"total": 6171, "input": 427, "output": 112, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6353, "input": 651, "output": 70, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 8203, "input": 2426, "output": 136, "reasoning": 9, "cache": {"write": 0, "read": 5632}}, {"total": 9441, "input": 3232, "output": 577, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 12197, "input": 4314, "output": 191, "reasoning": 12, "cache": {"write": 0, "read": 7680}}, {"total": 13084, "input": 1161, "output": 147, "reasoning": 0, "cache": {"write": 0, "read": 11776}}, {"total": 13720, "input": 396, "output": 524, "reasoning": 0, "cache": {"write": 0, "read": 12800}}, {"total": 14077, "input": 5295, "output": 78, "reasoning": 0, "cache": {"write": 0, "read": 8704}}, {"total": 14259, "input": 365, "output": 70, "reasoning": 0, "cache": {"write": 0, "read": 13824}}, {"total": 14364, "input": 463, "output": 77, "reasoning": 0, "cache": {"write": 0, "read": 13824}}, {"total": 14528, "input": 651, "output": 53, "reasoning": 0, "cache": {"write": 0, "read": 13824}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 21437, "turns": 11, "cmd": ["opencode", "run", "--dir", "/bench_opencode_make-ci-green_geowkk3b", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784478596.626767, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784478596.6286452, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784478596.629141, "sha256": ""}, "catalog/inventory.py": {"bytes": 744, "mtime": 1784478596.6295176, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784478596.629926, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784478596.630397, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:make-ci-green:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T12:30:07", "harness": "opencode", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 41.907, "t_env_setup_s": 0.005, "t_agent_s": 41.907, "t_checker_s": 0.227, "tokens": 13040, "tokens_input_uncached": 11196, "tokens_cache_read": 43520, "tokens_cache_write": 0, "tokens_output": 1844, "tokens_reasoning": 129, "usage_raw": [{"total": 6215, "input": 427, "output": 156, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 9339, "input": 2947, "output": 748, "reasoning": 12, "cache": {"write": 0, "read": 5632}}, {"total": 13379, "input": 4950, "output": 632, "reasoning": 117, "cache": {"write": 0, "read": 7680}}, {"total": 13762, "input": 1882, "output": 104, "reasoning": 0, "cache": {"write": 0, "read": 11776}}, {"total": 13865, "input": 990, "output": 75, "reasoning": 0, "cache": {"write": 0, "read": 12800}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 13040, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_make-ci-green_0jo69_aw", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784478643.6900802, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784478643.691932, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784478643.6923244, "sha256": ""}, "catalog/inventory.py": {"bytes": 785, "mtime": 1784478643.6926455, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784478643.692937, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784478643.6931813, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:make-it-run:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T12:30:49", "harness": "opencode", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 18.046, "t_env_setup_s": 0.002, "t_agent_s": 18.046, "t_checker_s": 0.163, "tokens": 3764, "tokens_input_uncached": 3175, "tokens_cache_read": 30208, "tokens_cache_write": 0, "tokens_output": 589, "tokens_reasoning": 0, "usage_raw": [{"total": 6210, "input": 409, "output": 169, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6530, "input": 764, "output": 134, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6946, "input": 1127, "output": 187, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7119, "input": 397, "output": 66, "reasoning": 0, "cache": {"write": 0, "read": 6656}}, {"total": 7167, "input": 478, "output": 33, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 3764, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_make-it-run_v9c8w1wk", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784478663.7338977, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784478663.7319598, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:make-it-run:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:31:07", "harness": "opencode", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 19.483, "t_env_setup_s": 0.002, "t_agent_s": 19.483, "t_checker_s": 0.163, "tokens": 4484, "tokens_input_uncached": 3947, "tokens_cache_read": 29184, "tokens_cache_write": 0, "tokens_output": 537, "tokens_reasoning": 0, "usage_raw": [{"total": 6162, "input": 407, "output": 123, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6472, "input": 710, "output": 130, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6884, "input": 1065, "output": 187, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7051, "input": 1355, "output": 64, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7099, "input": 410, "output": 33, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 4484, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_make-it-run_n3loltw5", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784478682.8640509, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784478682.8624516, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:make-it-run:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T12:31:27", "harness": "opencode", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 19.846, "t_env_setup_s": 0.002, "t_agent_s": 19.846, "t_checker_s": 0.164, "tokens": 4587, "tokens_input_uncached": 4027, "tokens_cache_read": 29184, "tokens_cache_write": 0, "tokens_output": 560, "tokens_reasoning": 9, "usage_raw": [{"total": 6163, "input": 408, "output": 123, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6489, "input": 714, "output": 134, "reasoning": 9, "cache": {"write": 0, "read": 5632}}, {"total": 6907, "input": 1084, "output": 191, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7082, "input": 1380, "output": 70, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7130, "input": 441, "output": 33, "reasoning": 0, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 4587, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_make-it-run_zfjt6c_z", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784478702.7004895, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784478702.698123, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:misleading-error:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T12:31:47", "harness": "opencode", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 22.937, "t_env_setup_s": 0.003, "t_agent_s": 22.937, "t_checker_s": 0.163, "tokens": 6252, "tokens_input_uncached": 5535, "tokens_cache_read": 31232, "tokens_cache_write": 0, "tokens_output": 717, "tokens_reasoning": 18, "usage_raw": [{"total": 6247, "input": 490, "output": 125, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7054, "input": 1118, "output": 304, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7956, "input": 1121, "output": 161, "reasoning": 18, "cache": {"write": 0, "read": 6656}}, {"total": 8083, "input": 337, "output": 66, "reasoning": 0, "cache": {"write": 0, "read": 7680}}, {"total": 8144, "input": 2469, "output": 43, "reasoning": 0, "cache": {"write": 0, "read": 5632}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 6252, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_misleading-error_fomna6v8", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784478725.3485105, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:misleading-error:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:32:10", "harness": "opencode", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 33.033, "t_env_setup_s": 0.002, "t_agent_s": 33.033, "t_checker_s": 0.163, "tokens": 5126, "tokens_input_uncached": 4439, "tokens_cache_read": 32256, "tokens_cache_write": 0, "tokens_output": 687, "tokens_reasoning": 19, "usage_raw": [{"total": 6218, "input": 490, "output": 96, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7036, "input": 1100, "output": 304, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7938, "input": 2127, "output": 160, "reasoning": 19, "cache": {"write": 0, "read": 5632}}, {"total": 8065, "input": 319, "output": 66, "reasoning": 0, "cache": {"write": 0, "read": 7680}}, {"total": 8125, "input": 403, "output": 42, "reasoning": 0, "cache": {"write": 0, "read": 7680}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 5126, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_misleading-error_dhiixf3a", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784478759.3748527, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:misleading-error:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T12:32:43", "harness": "opencode", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 21.818, "t_env_setup_s": 0.003, "t_agent_s": 21.818, "t_checker_s": 0.159, "tokens": 4287, "tokens_input_uncached": 3565, "tokens_cache_read": 33280, "tokens_cache_write": 0, "tokens_output": 722, "tokens_reasoning": 17, "usage_raw": [{"total": 6247, "input": 491, "output": 124, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7070, "input": 1129, "output": 309, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7977, "input": 1142, "output": 162, "reasoning": 17, "cache": {"write": 0, "read": 6656}}, {"total": 8106, "input": 359, "output": 67, "reasoning": 0, "cache": {"write": 0, "read": 7680}}, {"total": 8167, "input": 444, "output": 43, "reasoning": 0, "cache": {"write": 0, "read": 7680}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 4287, "turns": 5, "cmd": ["opencode", "run", "--dir", "/bench_opencode_misleading-error_y3e8b5ul", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784478781.4235306, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:taskflow:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T12:33:05", "harness": "opencode", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 60.137, "t_env_setup_s": 0.01, "t_agent_s": 60.137, "t_checker_s": 0.162, "tokens": 46134, "tokens_input_uncached": 44084, "tokens_cache_read": 240128, "tokens_cache_write": 0, "tokens_output": 2050, "tokens_reasoning": 129, "usage_raw": [{"total": 6433, "input": 695, "output": 106, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6615, "input": 912, "output": 71, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 9422, "input": 3703, "output": 79, "reasoning": 8, "cache": {"write": 0, "read": 5632}}, {"total": 9584, "input": 829, "output": 51, "reasoning": 0, "cache": {"write": 0, "read": 8704}}, {"total": 11786, "input": 2601, "output": 481, "reasoning": 0, "cache": {"write": 0, "read": 8704}}, {"total": 31469, "input": 20131, "output": 539, "reasoning": 47, "cache": {"write": 0, "read": 10752}}, {"total": 41662, "input": 11106, "output": 299, "reasoning": 49, "cache": {"write": 0, "read": 30208}}, {"total": 42076, "input": 1551, "output": 77, "reasoning": 0, "cache": {"write": 0, "read": 40448}}, {"total": 42258, "input": 715, "output": 71, "reasoning": 0, "cache": {"write": 0, "read": 41472}}, {"total": 42389, "input": 814, "output": 78, "reasoning": 25, "cache": {"write": 0, "read": 41472}}, {"total": 42568, "input": 1027, "output": 69, "reasoning": 0, "cache": {"write": 0, "read": 41472}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 46134, "turns": 11, "cmd": ["opencode", "run", "--dir", "/bench_opencode_taskflow_k4_m_6n4", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, "sha256": ""}, "tests/test_resources.py": {"bytes": 773, "mtime": 1784160991.2269812, "sha256": ""}, "tests/test_retry.py": {"bytes": 833, "mtime": 1784160991.2270508, "sha256": ""}, "tests/test_statemachine.py": {"bytes": 567, "mtime": 1784160991.2271175, "sha256": ""}, "taskflow/__init__.py": {"bytes": 2080, "mtime": 1784160991.2241614, "sha256": ""}, "taskflow/analysis.py": {"bytes": 8183, "mtime": 1784160991.224232, "sha256": ""}, "taskflow/builder.py": {"bytes": 8904, "mtime": 1784160991.2243223, "sha256": ""}, "taskflow/cli.py": {"bytes": 7365, "mtime": 1784160991.2243776, "sha256": ""}, "taskflow/config.py": {"bytes": 6988, "mtime": 1784478829.7652102, "sha256": ""}, "taskflow/dag.py": {"bytes": 15744, "mtime": 1784160991.224575, "sha256": ""}, "taskflow/dag_algorithms.py": {"bytes": 12614, "mtime": 1784160991.2246811, "sha256": ""}, "taskflow/diagnostics.py": {"bytes": 7380, "mtime": 1784160991.224753, "sha256": ""}, "taskflow/events.py": {"bytes": 6312, "mtime": 1784478829.768459, "sha256": ""}, "taskflow/export.py": {"bytes": 5319, "mtime": 1784160991.2248626, "sha256": ""}, "taskflow/filters.py": {"bytes": 8531, "mtime": 1784160991.2249525, "sha256": ""}, "taskflow/history.py": {"bytes": 8566, "mtime": 1784160991.2250726, "sha256": ""}, "taskflow/hooks.py": {"bytes": 10205, "mtime": 1784160991.2251635, "sha256": ""}, "taskflow/metrics.py": {"bytes": 11622, "mtime": 1784160991.2252398, "sha256": ""}, "taskflow/model.py": {"bytes": 16049, "mtime": 1784160991.2253256, "sha256": ""}, "taskflow/planner.py": {"bytes": 11805, "mtime": 1784160991.225468, "sha256": ""}, "taskflow/policies.py": {"bytes": 10634, "mtime": 1784160991.2255614, "sha256": ""}, "taskflow/queue.py": {"bytes": 5687, "mtime": 1784478829.7672784, "sha256": ""}, "taskflow/reporting.py": {"bytes": 9271, "mtime": 1784160991.2257166, "sha256": ""}, "taskflow/resources.py": {"bytes": 7281, "mtime": 1784478829.7687392, "sha256": ""}, "taskflow/retry.py": {"bytes": 6056, "mtime": 1784478829.7680018, "sha256": ""}, "taskflow/runner.py": {"bytes": 4452, "mtime": 1784160991.2259035, "sha256": ""}, "taskflow/scheduler.py": {"bytes": 14635, "mtime": 1784478829.7692041, "sha256": ""}, "taskflow/serialization.py": {"bytes": 9103, "mtime": 1784160991.2260818, "sha256": ""}, "taskflow/simulation.py": {"bytes": 7494, "mtime": 1784160991.226148, "sha256": ""}, "taskflow/statemachine.py": {"bytes": 6500, "mtime": 1784478829.7689524, "sha256": ""}, "taskflow/stats.py": {"bytes": 12341, "mtime": 1784160991.226304, "sha256": ""}, "taskflow/templating.py": {"bytes": 8884, "mtime": 1784160991.2263978, "sha256": ""}, "taskflow/timeutil.py": {"bytes": 9180, "mtime": 1784160991.2264779, "sha256": ""}, "taskflow/validation.py": {"bytes": 12447, "mtime": 1784160991.2265797, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:taskflow:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:34:06", "harness": "opencode", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 63.495, "t_env_setup_s": 0.01, "t_agent_s": 63.495, "t_checker_s": 0.229, "tokens": 44796, "tokens_input_uncached": 42274, "tokens_cache_read": 236544, "tokens_cache_write": 0, "tokens_output": 2522, "tokens_reasoning": 144, "usage_raw": [{"total": 6443, "input": 695, "output": 116, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6762, "input": 926, "output": 204, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 11295, "input": 5569, "output": 84, "reasoning": 10, "cache": {"write": 0, "read": 5632}}, {"total": 11891, "input": 658, "output": 481, "reasoning": 0, "cache": {"write": 0, "read": 10752}}, {"total": 30026, "input": 18702, "output": 551, "reasoning": 21, "cache": {"write": 0, "read": 10752}}, {"total": 42448, "input": 12537, "output": 635, "reasoning": 92, "cache": {"write": 0, "read": 29184}}, {"total": 42867, "input": 1313, "output": 82, "reasoning": 0, "cache": {"write": 0, "read": 41472}}, {"total": 43053, "input": 486, "output": 71, "reasoning": 0, "cache": {"write": 0, "read": 42496}}, {"total": 43185, "input": 585, "output": 83, "reasoning": 21, "cache": {"write": 0, "read": 42496}}, {"total": 43370, "input": 803, "output": 71, "reasoning": 0, "cache": {"write": 0, "read": 42496}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 44796, "turns": 10, "cmd": ["opencode", "run", "--dir", "/bench_opencode_taskflow_6o7cvcvh", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, 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"openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, "tests/test_queue.py": {"bytes": 823, "mtime": 1784160991.2269244, 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1784478960.7125187, "sha256": ""}, "taskflow/runner.py": {"bytes": 4452, "mtime": 1784160991.2259035, "sha256": ""}, "taskflow/scheduler.py": {"bytes": 14635, "mtime": 1784478960.7135327, "sha256": ""}, "taskflow/serialization.py": {"bytes": 9103, "mtime": 1784160991.2260818, "sha256": ""}, "taskflow/simulation.py": {"bytes": 7494, "mtime": 1784160991.226148, "sha256": ""}, "taskflow/statemachine.py": {"bytes": 6500, "mtime": 1784478960.7132967, "sha256": ""}, "taskflow/stats.py": {"bytes": 12341, "mtime": 1784160991.226304, "sha256": ""}, "taskflow/templating.py": {"bytes": 8884, "mtime": 1784160991.2263978, "sha256": ""}, "taskflow/timeutil.py": {"bytes": 9180, "mtime": 1784160991.2264779, "sha256": ""}, "taskflow/validation.py": {"bytes": 12447, "mtime": 1784160991.2265797, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:webcore:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T12:36:16", "harness": "opencode", "model": "gpt-5.6-sol", "task": "webcore", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 302.048, "t_env_setup_s": 0.013, "t_agent_s": 302.048, "t_checker_s": 0.228, "tokens": 58519, "tokens_input_uncached": 44155, "tokens_cache_read": 460800, "tokens_cache_write": 0, "tokens_output": 14364, "tokens_reasoning": 3641, "usage_raw": [{"total": 7987, "input": 2208, "output": 134, "reasoning": 13, "cache": {"write": 0, "read": 5632}}, {"total": 8265, "input": 2500, "output": 133, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 10703, "input": 2589, "output": 418, "reasoning": 16, "cache": {"write": 0, "read": 7680}}, {"total": 21174, "input": 11090, "output": 339, "reasoning": 17, "cache": {"write": 0, "read": 9728}}, {"total": 24778, "input": 2991, "output": 167, "reasoning": 1652, "cache": {"write": 0, "read": 19968}}, {"total": 27490, "input": 2907, "output": 2567, "reasoning": 0, "cache": {"write": 0, "read": 22016}}, {"total": 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0, "read": 35328}}, {"total": 37129, "input": 646, "output": 99, "reasoning": 32, "cache": {"write": 0, "read": 36352}}, {"total": 37433, "input": 921, "output": 160, "reasoning": 0, "cache": {"write": 0, "read": 36352}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 58519, "turns": 18, "cmd": ["opencode", "run", "--dir", "/bench_opencode_webcore_ih8mc5jh", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, "test_routing_features.py": {"bytes": 4850, "mtime": 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{"write": 0, "read": 42496}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 62039, "turns": 18, "cmd": ["opencode", "run", "--dir", "/bench_opencode_webcore_fsb2o2y7", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, "test_routing_features.py": {"bytes": 5109, "mtime": 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layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "1.18.3 (/opt/homebrew/bin/opencode)", "harness_version_source": "host", "failure_class": "solved", "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4648, "mtime": 1784479969.0382333, "sha256": ""}, "webcore/__init__.py": {"bytes": 2140, "mtime": 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"/dev/openbench/.bench-tmp/bench_instr_ait3_krq.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. 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You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784480103.2962008, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784480080.2959313, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784480097.0227742, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:55:09", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 47.535, "t_env_setup_s": 0.104, "t_agent_s": 47.376, "t_checker_s": 0.226, "tokens": 15302, "tokens_input_uncached": 13629, "tokens_cache_read": 56832, "tokens_cache_write": 0, "tokens_output": 1673, "tokens_reasoning": 463, "usage_raw": [{"total": 6222, "input": 2511, "output": 127, "reasoning": 0, "cache": {"write": 0, "read": 3584}}, {"total": 6538, "input": 737, "output": 142, "reasoning": 27, "cache": {"write": 0, "read": 5632}}, {"total": 6786, "input": 3028, "output": 130, "reasoning": 44, "cache": {"write": 0, "read": 3584}}, {"total": 7862, "input": 1899, "output": 140, "reasoning": 191, "cache": {"write": 0, "read": 5632}}, {"total": 8133, "input": 1220, "output": 143, "reasoning": 114, "cache": {"write": 0, "read": 6656}}, {"total": 8757, "input": 791, "output": 231, "reasoning": 55, "cache": {"write": 0, "read": 7680}}, {"total": 8942, "input": 1091, "output": 159, "reasoning": 12, "cache": {"write": 0, "read": 7680}}, {"total": 9414, "input": 1620, "output": 94, "reasoning": 20, "cache": {"write": 0, "read": 7680}}, {"total": 9480, "input": 732, "output": 44, "reasoning": 0, "cache": {"write": 0, "read": 8704}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 15302, "turns": 9, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_8fc09a84", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_db-wal-recovery_i523kc0u:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_hlwff1i_.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784480146.0975232, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784480130.3933606, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784480146.0956767, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T12:55:56", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 53.959, "t_env_setup_s": 0.097, "t_agent_s": 53.813, "t_checker_s": 0.218, "tokens": 12080, "tokens_input_uncached": 10085, "tokens_cache_read": 76288, "tokens_cache_write": 0, "tokens_output": 1995, "tokens_reasoning": 456, "usage_raw": [{"total": 6218, "input": 463, "output": 123, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6414, "input": 699, "output": 83, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6742, "input": 848, "output": 189, "reasoning": 73, "cache": {"write": 0, "read": 5632}}, {"total": 7078, "input": 1227, "output": 151, "reasoning": 68, "cache": {"write": 0, "read": 5632}}, {"total": 8068, "input": 1044, "output": 164, "reasoning": 204, "cache": {"write": 0, "read": 6656}}, {"total": 8245, "input": 1525, "output": 64, "reasoning": 0, "cache": {"write": 0, "read": 6656}}, {"total": 8467, "input": 579, "output": 133, "reasoning": 75, "cache": {"write": 0, "read": 7680}}, {"total": 9023, "input": 990, "output": 317, "reasoning": 36, "cache": {"write": 0, "read": 7680}}, {"total": 9235, "input": 1366, "output": 189, "reasoning": 0, "cache": {"write": 0, "read": 7680}}, {"total": 9359, "input": 577, "output": 78, "reasoning": 0, "cache": {"write": 0, "read": 8704}}, {"total": 9519, "input": 767, "output": 48, "reasoning": 0, "cache": {"write": 0, "read": 8704}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 12080, "turns": 11, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_b8d97a18", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_db-wal-recovery_krx53eyi:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ldem6my9.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784480192.233423, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784480187.5730402, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784480199.9488494, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/extract-elf:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T12:56:51", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 92.908, "t_env_setup_s": 0.087, "t_agent_s": 92.767, "t_checker_s": 0.338, "tokens": 21437, "tokens_input_uncached": 17033, "tokens_cache_read": 69120, "tokens_cache_write": 0, "tokens_output": 4404, "tokens_reasoning": 1499, "usage_raw": [{"total": 6238, "input": 2482, "output": 142, "reasoning": 30, "cache": {"write": 0, "read": 3584}}, {"total": 6470, "input": 666, "output": 86, "reasoning": 86, "cache": {"write": 0, "read": 5632}}, {"total": 6607, "input": 871, "output": 54, "reasoning": 50, "cache": {"write": 0, "read": 5632}}, {"total": 7127, "input": 998, "output": 437, "reasoning": 60, "cache": {"write": 0, "read": 5632}}, {"total": 8357, "input": 2016, "output": 496, "reasoning": 213, "cache": {"write": 0, "read": 5632}}, {"total": 11637, "input": 4833, "output": 125, "reasoning": 23, "cache": {"write": 0, "read": 6656}}, {"total": 14486, "input": 1515, "output": 1347, "reasoning": 872, "cache": {"write": 0, "read": 10752}}, {"total": 14687, "input": 2733, "output": 148, "reasoning": 30, "cache": {"write": 0, "read": 11776}}, {"total": 14948, "input": 919, "output": 70, "reasoning": 135, "cache": {"write": 0, "read": 13824}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 21437, "turns": 9, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_1d948b20", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_extract-elf_5446wl1n:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_0ufch96i.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3344, "mtime": 1784480293.9611814, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/extract-elf:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T12:58:24", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 111.934, "t_env_setup_s": 0.084, "t_agent_s": 111.805, "t_checker_s": 0.215, "tokens": 26246, "tokens_input_uncached": 21273, "tokens_cache_read": 87040, "tokens_cache_write": 0, "tokens_output": 4973, "tokens_reasoning": 2000, "usage_raw": [{"total": 6281, "input": 2482, "output": 176, "reasoning": 39, "cache": {"write": 0, "read": 3584}}, {"total": 6527, "input": 732, "output": 95, "reasoning": 68, "cache": {"write": 0, "read": 5632}}, {"total": 6675, "input": 2974, "output": 53, "reasoning": 64, "cache": {"write": 0, "read": 3584}}, {"total": 6870, "input": 1066, "output": 109, "reasoning": 63, "cache": {"write": 0, "read": 5632}}, {"total": 7649, "input": 1335, "output": 563, "reasoning": 119, "cache": {"write": 0, "read": 5632}}, {"total": 12306, "input": 5286, "output": 289, "reasoning": 75, "cache": {"write": 0, "read": 6656}}, {"total": 14919, "input": 2266, "output": 179, "reasoning": 698, "cache": {"write": 0, "read": 11776}}, {"total": 16445, "input": 1136, "output": 1257, "reasoning": 228, "cache": {"write": 0, "read": 13824}}, {"total": 16643, "input": 1620, "output": 158, "reasoning": 17, "cache": {"write": 0, "read": 14848}}, {"total": 18971, "input": 2376, "output": 94, "reasoning": 629, "cache": {"write": 0, "read": 15872}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 26246, "turns": 10, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_c216967f", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_extract-elf_p1gv02eb:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_iw36m1zn.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3244, "mtime": 1784480395.9642255, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/extract-elf:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T13:00:16", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 103.85, "t_env_setup_s": 0.08, "t_agent_s": 103.731, "t_checker_s": 0.272, "tokens": 22968, "tokens_input_uncached": 18500, "tokens_cache_read": 82944, "tokens_cache_write": 0, "tokens_output": 4468, "tokens_reasoning": 1551, "usage_raw": [{"total": 6235, "input": 434, "output": 139, "reasoning": 30, "cache": {"write": 0, "read": 5632}}, {"total": 6443, "input": 662, "output": 102, "reasoning": 47, "cache": {"write": 0, "read": 5632}}, {"total": 6956, "input": 1012, "output": 269, "reasoning": 43, "cache": {"write": 0, "read": 5632}}, {"total": 7763, "input": 3956, "output": 67, "reasoning": 156, "cache": {"write": 0, "read": 3584}}, {"total": 8087, "input": 1136, "output": 267, "reasoning": 28, "cache": {"write": 0, "read": 6656}}, {"total": 10410, "input": 2439, "output": 263, "reasoning": 28, "cache": {"write": 0, "read": 7680}}, {"total": 12138, "input": 1961, "output": 168, "reasoning": 281, "cache": {"write": 0, "read": 9728}}, {"total": 15647, "input": 2812, "output": 1295, "reasoning": 788, "cache": {"write": 0, "read": 10752}}, {"total": 15974, "input": 2870, "output": 281, "reasoning": 23, "cache": {"write": 0, "read": 12800}}, {"total": 16259, "input": 1218, "output": 66, "reasoning": 127, "cache": {"write": 0, "read": 14848}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 22968, "turns": 10, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_6ee37025", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_extract-elf_vlb94b1h:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_u7iz1th3.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3464, "mtime": 1784480507.5234528, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T13:02:00", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 1, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 75.124, "t_env_setup_s": 0.084, "t_agent_s": 74.987, "t_checker_s": 0.172, "tokens": 3567, "tokens_input_uncached": 2797, "tokens_cache_read": 17920, "tokens_cache_write": 0, "tokens_output": 770, "tokens_reasoning": 402, "usage_raw": [{"total": 6135, "input": 452, "output": 51, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7580, "input": 1396, "output": 158, "reasoning": 394, "cache": {"write": 0, "read": 5632}}, {"total": 7772, "input": 949, "output": 159, "reasoning": 8, "cache": {"write": 0, "read": 6656}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 3567, "turns": 3, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_0b400673", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_feal-differential-cryptanalysis_armt5g1z:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_ghsz7u4j.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T13:03:15", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 2, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 27.17, "t_env_setup_s": 0.09, "t_agent_s": 27.032, "t_checker_s": 0.173, "tokens": 545, "tokens_input_uncached": 452, "tokens_cache_read": 5632, "tokens_cache_write": 0, "tokens_output": 93, "tokens_reasoning": 0, "usage_raw": [{"total": 6177, "input": 452, "output": 93, "reasoning": 0, "cache": {"write": 0, "read": 5632}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 545, "turns": 1, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_2ab57d87", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_feal-differential-cryptanalysis_55toetvc:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_kotqchmf.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T13:03:43", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 3, "success": true, "completed": false, "error": "exit 1", "wall_time_s": 114.195, "t_env_setup_s": 0.088, "t_agent_s": 114.042, "t_checker_s": 0.458, "tokens": 12762, "tokens_input_uncached": 10177, "tokens_cache_read": 34816, "tokens_cache_write": 0, "tokens_output": 2585, "tokens_reasoning": 1017, "usage_raw": [{"total": 6142, "input": 452, "output": 58, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6180, "input": 525, "output": 23, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 7701, "input": 1441, "output": 156, "reasoning": 472, "cache": {"write": 0, "read": 5632}}, {"total": 7890, "input": 1070, "output": 157, "reasoning": 7, "cache": {"write": 0, "read": 6656}}, {"total": 9715, "input": 4631, "output": 984, "reasoning": 516, "cache": {"write": 0, "read": 3584}}, {"total": 9950, "input": 2058, "output": 190, "reasoning": 22, "cache": {"write": 0, "read": 7680}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 12762, "turns": 6, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_0f6e8522", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_feal-differential-cryptanalysis_9qsm0hql:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_misknoo9.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 2076, "mtime": 1784480700.3828173, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/gcode-to-text:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T13:05:37", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 195.888, "t_env_setup_s": 0.085, "t_agent_s": 195.755, "t_checker_s": 0.165, "tokens": 119655, "tokens_input_uncached": 112238, "tokens_cache_read": 1110528, "tokens_cache_write": 0, "tokens_output": 7417, "tokens_reasoning": 3778, "usage_raw": [{"total": 6091, "input": 331, "output": 128, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6173, "input": 505, "output": 29, "reasoning": 7, "cache": {"write": 0, "read": 5632}}, {"total": 38548, "input": 32592, "output": 34, "reasoning": 290, "cache": {"write": 0, "read": 5632}}, {"total": 39294, "input": 1715, "output": 109, "reasoning": 94, "cache": {"write": 0, "read": 37376}}, {"total": 45051, "input": 5800, "output": 519, "reasoning": 332, "cache": {"write": 0, "read": 38400}}, {"total": 45116, "input": 1557, "output": 39, "reasoning": 0, "cache": {"write": 0, "read": 43520}}, {"total": 45191, "input": 597, "output": 42, "reasoning": 8, "cache": {"write": 0, "read": 44544}}, {"total": 45739, "input": 704, "output": 460, "reasoning": 31, "cache": {"write": 0, "read": 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{"write": 0, "read": 1536}}, {"total": 51356, "input": 2370, "output": 162, "reasoning": 184, "cache": {"write": 0, "read": 48640}}, {"total": 51422, "input": 694, "output": 40, "reasoning": 0, "cache": {"write": 0, "read": 50688}}, {"total": 51460, "input": 748, "output": 24, "reasoning": 0, "cache": {"write": 0, "read": 50688}}, {"total": 53952, "input": 1153, "output": 769, "reasoning": 1342, "cache": {"write": 0, "read": 50688}}, {"total": 54018, "input": 3290, "output": 40, "reasoning": 0, "cache": {"write": 0, "read": 50688}}, {"total": 54056, "input": 1296, "output": 24, "reasoning": 0, "cache": {"write": 0, "read": 52736}}, {"total": 54659, "input": 1658, "output": 104, "reasoning": 161, "cache": {"write": 0, "read": 52736}}, {"total": 54712, "input": 921, "output": 23, "reasoning": 8, "cache": {"write": 0, "read": 53760}}, {"total": 54786, "input": 1011, "output": 15, "reasoning": 0, "cache": {"write": 0, "read": 53760}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 119655, "turns": 27, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_17c1944b", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_gcode-to-text_unxt48oa:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_j2_qq9_e.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784480928.8200986, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/gcode-to-text:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T13:08:54", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 217.788, "t_env_setup_s": 0.089, "t_agent_s": 217.642, "t_checker_s": 0.236, "tokens": 139969, "tokens_input_uncached": 131731, 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"token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 139969, "turns": 28, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_ad094629", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_gcode-to-text_s3f91i_s:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_77610teu.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: out.txt does not contain the decoded printed text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 24, "mtime": 1784481146.592366, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/gcode-to-text:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T13:12:32", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 160.723, "t_env_setup_s": 0.088, "t_agent_s": 160.573, "t_checker_s": 0.235, "tokens": 108953, "tokens_input_uncached": 102200, "tokens_cache_read": 670720, "tokens_cache_write": 0, "tokens_output": 6753, "tokens_reasoning": 2364, "usage_raw": [{"total": 6086, "input": 331, "output": 123, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6156, "input": 480, "output": 28, "reasoning": 16, "cache": {"write": 0, "read": 5632}}, {"total": 11810, "input": 5833, "output": 104, "reasoning": 241, "cache": {"write": 0, "read": 5632}}, {"total": 12815, "input": 1106, "output": 942, "reasoning": 15, "cache": {"write": 0, "read": 10752}}, {"total": 12882, "input": 1066, "output": 40, "reasoning": 0, "cache": {"write": 0, "read": 11776}}, {"total": 21635, "input": 7813, "output": 1472, "reasoning": 574, "cache": {"write": 0, "read": 11776}}, {"total": 21712, "input": 2718, "output": 50, "reasoning": 0, "cache": {"write": 0, "read": 18944}}, {"total": 21793, "input": 734, "output": 67, "reasoning": 0, "cache": {"write": 0, "read": 20992}}, {"total": 45645, "input": 24570, "output": 48, "reasoning": 35, "cache": {"write": 0, "read": 20992}}, {"total": 46298, "input": 1115, "output": 450, "reasoning": 189, "cache": {"write": 0, "read": 44544}}, {"total": 46375, "input": 757, "output": 50, "reasoning": 0, "cache": {"write": 0, "read": 45568}}, {"total": 46413, "input": 821, "output": 24, "reasoning": 0, "cache": {"write": 0, "read": 45568}}, {"total": 46512, "input": 867, "output": 45, "reasoning": 32, "cache": {"write": 0, "read": 45568}}, {"total": 47038, "input": 958, "output": 482, "reasoning": 30, "cache": {"write": 0, "read": 45568}}, {"total": 47115, "input": 1497, "output": 50, "reasoning": 0, "cache": {"write": 0, "read": 45568}}, {"total": 47152, "input": 537, "output": 23, "reasoning": 0, "cache": {"write": 0, "read": 46592}}, {"total": 48016, "input": 45873, "output": 172, "reasoning": 435, "cache": {"write": 0, "read": 1536}}, {"total": 48093, "input": 1451, "output": 50, "reasoning": 0, "cache": {"write": 0, "read": 46592}}, {"total": 48130, "input": 491, "output": 23, "reasoning": 0, "cache": {"write": 0, "read": 47616}}, {"total": 49268, "input": 768, "output": 95, "reasoning": 789, "cache": {"write": 0, "read": 47616}}, {"total": 49321, "input": 1674, "output": 23, "reasoning": 8, "cache": {"write": 0, "read": 47616}}, {"total": 49408, "input": 740, "output": 28, "reasoning": 0, "cache": {"write": 0, "read": 48640}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 108953, "turns": 22, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_375f2da7", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_gcode-to-text_zlee7ldg:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_afv399xc.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784481306.5483592, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T13:15:13", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 176.69, "t_env_setup_s": 0.087, "t_agent_s": 176.55, "t_checker_s": 0.23, "tokens": 67748, "tokens_input_uncached": 60393, "tokens_cache_read": 354304, "tokens_cache_write": 0, "tokens_output": 7355, "tokens_reasoning": 2133, "usage_raw": [{"total": 6867, "input": 1098, "output": 120, "reasoning": 17, "cache": {"write": 0, "read": 5632}}, {"total": 7126, "input": 1351, "output": 143, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 13473, "input": 5890, "output": 415, "reasoning": 512, "cache": {"write": 0, "read": 6656}}, {"total": 14269, "input": 2028, "output": 428, "reasoning": 37, "cache": {"write": 0, "read": 11776}}, {"total": 15001, "input": 1573, "output": 398, "reasoning": 230, "cache": {"write": 0, "read": 12800}}, {"total": 28981, "input": 14301, "output": 715, "reasoning": 141, "cache": {"write": 0, "read": 13824}}, {"total": 30134, "input": 23738, "output": 653, "reasoning": 111, "cache": {"write": 0, "read": 5632}}, {"total": 31059, "input": 1251, "output": 263, "reasoning": 361, "cache": {"write": 0, "read": 29184}}, {"total": 32679, "input": 1834, "output": 155, "reasoning": 482, "cache": {"write": 0, "read": 30208}}, {"total": 33520, "input": 1563, "output": 725, "reasoning": 0, "cache": {"write": 0, "read": 31232}}, {"total": 33594, "input": 1294, "output": 37, "reasoning": 7, "cache": {"write": 0, "read": 32256}}, {"total": 33769, "input": 328, "output": 84, "reasoning": 77, "cache": {"write": 0, "read": 33280}}, {"total": 34665, "input": 605, "output": 780, "reasoning": 0, "cache": {"write": 0, "read": 33280}}, {"total": 35401, "input": 1990, "output": 26, "reasoning": 105, "cache": {"write": 0, "read": 33280}}, {"total": 35601, "input": 1161, "output": 83, "reasoning": 53, "cache": {"write": 0, "read": 34304}}, {"total": 35913, "input": 388, "output": 197, "reasoning": 0, "cache": {"write": 0, "read": 35328}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 67748, "turns": 16, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_59ee7343", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_llm-inference-batching-scheduler_32ofvalm:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_g71y9ms6.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/shape_aware_packer.py": {"bytes": 2136, "mtime": 1784481445.5548027, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784481448.742289, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784481448.7448146, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T13:18:10", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 134.666, "t_env_setup_s": 0.082, "t_agent_s": 134.526, "t_checker_s": 0.229, "tokens": 45956, "tokens_input_uncached": 40187, "tokens_cache_read": 210432, "tokens_cache_write": 0, "tokens_output": 5769, "tokens_reasoning": 1793, "usage_raw": [{"total": 6877, "input": 1098, "output": 123, "reasoning": 24, "cache": {"write": 0, "read": 5632}}, {"total": 7143, "input": 3413, "output": 146, "reasoning": 0, "cache": {"write": 0, "read": 3584}}, {"total": 13181, "input": 5930, "output": 321, "reasoning": 274, "cache": {"write": 0, "read": 6656}}, {"total": 13570, "input": 1430, "output": 356, "reasoning": 8, "cache": {"write": 0, "read": 11776}}, {"total": 14828, "input": 1121, "output": 430, "reasoning": 477, "cache": {"write": 0, "read": 12800}}, {"total": 15811, "input": 9641, "output": 342, "reasoning": 196, "cache": {"write": 0, "read": 5632}}, {"total": 17602, "input": 1917, "output": 459, "reasoning": 378, "cache": {"write": 0, "read": 14848}}, {"total": 19620, "input": 3292, "output": 180, "reasoning": 276, "cache": {"write": 0, "read": 15872}}, {"total": 19879, "input": 729, "output": 88, "reasoning": 118, "cache": {"write": 0, "read": 18944}}, {"total": 20102, "input": 6175, "output": 103, "reasoning": 0, "cache": {"write": 0, "read": 13824}}, {"total": 20562, "input": 1199, "output": 419, "reasoning": 0, "cache": {"write": 0, "read": 18944}}, {"total": 20696, "input": 608, "output": 88, "reasoning": 32, "cache": {"write": 0, "read": 19968}}, {"total": 21480, "input": 848, "output": 664, "reasoning": 0, "cache": {"write": 0, "read": 19968}}, {"total": 22374, "input": 2309, "output": 87, "reasoning": 10, "cache": {"write": 0, "read": 19968}}, {"total": 22663, "input": 477, "output": 170, "reasoning": 0, "cache": {"write": 0, "read": 22016}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 45956, "turns": 15, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_e1321e74", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_llm-inference-batching-scheduler_rbo71pnb:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr__xynk50d.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784481597.5784016, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 88800, "mtime": 1784481597.5827086, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T13:20:24", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 172.042, "t_env_setup_s": 0.087, "t_agent_s": 171.902, "t_checker_s": 0.233, "tokens": 53530, "tokens_input_uncached": 46098, "tokens_cache_read": 477696, "tokens_cache_write": 0, "tokens_output": 7432, "tokens_reasoning": 1853, "usage_raw": [{"total": 6863, "input": 1098, "output": 118, "reasoning": 15, "cache": {"write": 0, "read": 5632}}, {"total": 7123, "input": 1345, "output": 146, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 13143, "input": 6934, "output": 303, "reasoning": 274, "cache": {"write": 0, "read": 5632}}, {"total": 13381, "input": 1427, "output": 151, "reasoning": 27, "cache": {"write": 0, "read": 11776}}, {"total": 27050, "input": 13811, "output": 257, "reasoning": 182, "cache": {"write": 0, "read": 12800}}, {"total": 27743, "input": 1093, "output": 279, "reasoning": 259, "cache": {"write": 0, "read": 26112}}, {"total": 28344, "input": 1900, "output": 144, "reasoning": 188, "cache": {"write": 0, "read": 26112}}, {"total": 29109, "input": 1322, "output": 651, "reasoning": 0, "cache": {"write": 0, "read": 27136}}, {"total": 29757, "input": 979, "output": 590, "reasoning": 28, "cache": {"write": 0, "read": 28160}}, {"total": 30825, "input": 1778, "output": 519, "reasoning": 368, "cache": {"write": 0, "read": 28160}}, {"total": 30898, "input": 1667, "output": 47, "reasoning": 0, "cache": {"write": 0, "read": 29184}}, {"total": 32123, "input": 953, "output": 798, "reasoning": 164, "cache": {"write": 0, "read": 30208}}, {"total": 32196, "input": 1941, "output": 47, "reasoning": 0, "cache": {"write": 0, "read": 30208}}, {"total": 35918, "input": 4128, "output": 321, "reasoning": 237, "cache": {"write": 0, "read": 31232}}, {"total": 36547, "input": 1644, "output": 599, "reasoning": 0, "cache": {"write": 0, "read": 34304}}, {"total": 37000, "input": 1408, "output": 153, "reasoning": 111, "cache": {"write": 0, "read": 35328}}, {"total": 37303, "input": 762, "output": 189, "reasoning": 0, "cache": {"write": 0, "read": 36352}}, {"total": 37801, "input": 1370, "output": 79, "reasoning": 0, "cache": {"write": 0, "read": 36352}}, {"total": 38102, "input": 538, "output": 188, "reasoning": 0, "cache": {"write": 0, "read": 37376}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 53530, "turns": 19, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_dbf8ce80", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_llm-inference-batching-scheduler_aul5xore:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_7csgijc1.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/shape_aware_packer.py": {"bytes": 2204, "mtime": 1784481761.9272711, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784481775.2062027, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784481775.2098446, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/raman-fitting:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T13:23:17", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 232.306, "t_env_setup_s": 0.082, "t_agent_s": 232.171, "t_checker_s": 0.166, "tokens": 35736, "tokens_input_uncached": 26729, "tokens_cache_read": 180224, "tokens_cache_write": 0, "tokens_output": 9007, "tokens_reasoning": 4264, "usage_raw": [{"total": 6207, "input": 445, "output": 106, "reasoning": 24, "cache": {"write": 0, "read": 5632}}, {"total": 6327, "input": 609, "output": 74, "reasoning": 12, "cache": {"write": 0, "read": 5632}}, {"total": 6467, "input": 752, "output": 36, "reasoning": 47, "cache": {"write": 0, "read": 5632}}, {"total": 6538, "input": 861, "output": 39, "reasoning": 6, "cache": {"write": 0, "read": 5632}}, {"total": 7262, "input": 1199, "output": 198, "reasoning": 233, "cache": {"write": 0, "read": 5632}}, {"total": 7464, "input": 645, "output": 156, "reasoning": 7, "cache": {"write": 0, "read": 6656}}, {"total": 7712, "input": 851, "output": 147, "reasoning": 58, "cache": {"write": 0, "read": 6656}}, {"total": 8429, "input": 1244, "output": 273, "reasoning": 256, "cache": {"write": 0, "read": 6656}}, {"total": 9617, "input": 1588, "output": 110, "reasoning": 239, "cache": {"write": 0, "read": 7680}}, {"total": 10509, "input": 1574, "output": 32, "reasoning": 199, "cache": {"write": 0, "read": 8704}}, {"total": 11689, "input": 802, "output": 759, "reasoning": 400, "cache": {"write": 0, "read": 9728}}, {"total": 13444, "input": 2577, "output": 596, "reasoning": 543, "cache": {"write": 0, "read": 9728}}, {"total": 13964, "input": 2027, "output": 130, "reasoning": 31, "cache": {"write": 0, "read": 11776}}, {"total": 16374, "input": 1825, "output": 855, "reasoning": 894, "cache": {"write": 0, "read": 12800}}, {"total": 19287, "input": 3907, "output": 890, "reasoning": 666, "cache": {"write": 0, "read": 13824}}, {"total": 21408, "input": 3646, "output": 241, "reasoning": 625, "cache": {"write": 0, "read": 16896}}, {"total": 21525, "input": 1462, "output": 71, "reasoning": 24, "cache": {"write": 0, "read": 19968}}, {"total": 21737, "input": 715, "output": 30, "reasoning": 0, "cache": {"write": 0, "read": 20992}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 35736, "turns": 18, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_3f96b417", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_raman-fitting_042mcm47:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_5kfyn1gz.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 223, "mtime": 1784482019.8736172, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/raman-fitting:gpt-5.6-sol:trial2", "ts_iso": "2026-07-19T13:27:09", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 164.138, "t_env_setup_s": 0.085, "t_agent_s": 163.988, "t_checker_s": 0.214, "tokens": 33634, "tokens_input_uncached": 26439, "tokens_cache_read": 179712, "tokens_cache_write": 0, "tokens_output": 7195, "tokens_reasoning": 3118, "usage_raw": [{"total": 6216, "input": 445, "output": 102, "reasoning": 37, "cache": {"write": 0, "read": 5632}}, {"total": 6271, "input": 612, "output": 27, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6395, "input": 660, "output": 76, "reasoning": 27, "cache": {"write": 0, "read": 5632}}, {"total": 6511, "input": 806, "output": 36, "reasoning": 37, "cache": {"write": 0, "read": 5632}}, {"total": 6583, "input": 2953, "output": 39, "reasoning": 7, "cache": {"write": 0, "read": 3584}}, {"total": 7245, "input": 1314, "output": 164, "reasoning": 135, "cache": {"write": 0, "read": 5632}}, {"total": 7442, "input": 614, "output": 165, "reasoning": 7, "cache": {"write": 0, "read": 6656}}, {"total": 7649, "input": 829, "output": 116, "reasoning": 48, "cache": {"write": 0, "read": 6656}}, {"total": 7906, "input": 1045, "output": 182, "reasoning": 23, "cache": {"write": 0, "read": 6656}}, {"total": 9155, "input": 1523, "output": 323, "reasoning": 653, "cache": {"write": 0, "read": 6656}}, {"total": 11200, "input": 2394, "output": 200, "reasoning": 926, "cache": {"write": 0, "read": 7680}}, {"total": 13099, "input": 2215, "output": 824, "reasoning": 332, "cache": {"write": 0, "read": 9728}}, {"total": 14128, "input": 2191, "output": 118, "reasoning": 43, "cache": {"write": 0, "read": 11776}}, {"total": 15179, "input": 747, "output": 492, "reasoning": 116, "cache": {"write": 0, "read": 13824}}, {"total": 16413, "input": 1914, "output": 383, "reasoning": 292, "cache": {"write": 0, "read": 13824}}, {"total": 17610, "input": 1836, "output": 513, "reasoning": 413, "cache": {"write": 0, "read": 14848}}, {"total": 17940, "input": 1893, "output": 166, "reasoning": 9, "cache": {"write": 0, "read": 15872}}, {"total": 18093, "input": 1066, "output": 118, "reasoning": 13, "cache": {"write": 0, "read": 16896}}, {"total": 18311, "input": 1382, "output": 33, "reasoning": 0, "cache": {"write": 0, "read": 16896}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 33634, "turns": 19, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_70a8c94e", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_raman-fitting_64mh2ypq:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_f25_u6fo.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 233, "mtime": 1784482187.7392027, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/raman-fitting:gpt-5.6-sol:trial3", "ts_iso": "2026-07-19T13:29:54", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 158.273, "t_env_setup_s": 0.084, "t_agent_s": 158.138, "t_checker_s": 0.224, "tokens": 33591, "tokens_input_uncached": 27023, "tokens_cache_read": 218624, "tokens_cache_write": 0, "tokens_output": 6568, "tokens_reasoning": 3127, "usage_raw": [{"total": 6193, "input": 445, "output": 100, "reasoning": 16, "cache": {"write": 0, "read": 5632}}, {"total": 6248, "input": 589, "output": 27, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 6397, "input": 637, "output": 102, "reasoning": 26, "cache": {"write": 0, "read": 5632}}, {"total": 6749, "input": 997, "output": 89, "reasoning": 31, "cache": {"write": 0, "read": 5632}}, {"total": 7685, "input": 1716, "output": 125, "reasoning": 212, "cache": {"write": 0, "read": 5632}}, {"total": 9026, "input": 1986, "output": 136, "reasoning": 248, "cache": {"write": 0, "read": 6656}}, {"total": 12453, "input": 4387, "output": 169, "reasoning": 217, "cache": {"write": 0, "read": 7680}}, {"total": 13341, "input": 1291, "output": 112, "reasoning": 162, "cache": {"write": 0, "read": 11776}}, {"total": 14572, "input": 1110, "output": 170, "reasoning": 492, "cache": {"write": 0, "read": 12800}}, {"total": 17348, "input": 3101, "output": 206, "reasoning": 217, "cache": {"write": 0, "read": 13824}}, {"total": 17812, "input": 1746, "output": 149, "reasoning": 45, "cache": {"write": 0, "read": 15872}}, {"total": 20080, "input": 1889, "output": 779, "reasoning": 516, "cache": {"write": 0, "read": 16896}}, {"total": 20878, "input": 2542, "output": 113, "reasoning": 303, "cache": {"write": 0, "read": 17920}}, {"total": 22700, "input": 1350, "output": 823, "reasoning": 559, "cache": {"write": 0, "read": 19968}}, {"total": 23430, "input": 2134, "output": 240, "reasoning": 64, "cache": {"write": 0, "read": 20992}}, {"total": 23542, "input": 412, "output": 71, "reasoning": 19, "cache": {"write": 0, "read": 23040}}, {"total": 23761, "input": 691, "output": 30, "reasoning": 0, "cache": {"write": 0, "read": 23040}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 33591, "turns": 17, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_43b106b8", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_raman-fitting_38m0pn56:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_w7vqyp29.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 241, "mtime": 1784482346.3595033, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "opencode:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial1", "ts_iso": "2026-07-19T13:32:32", "harness": "opencode", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 1086.791, "t_env_setup_s": 0.096, "t_agent_s": 1086.64, "t_checker_s": 25.174, "tokens": 70485, "tokens_input_uncached": 54039, "tokens_cache_read": 804864, "tokens_cache_write": 0, "tokens_output": 16446, "tokens_reasoning": 7921, "usage_raw": [{"total": 6476, "input": 691, "output": 140, "reasoning": 13, "cache": {"write": 0, "read": 5632}}, {"total": 6672, "input": 965, "output": 75, "reasoning": 0, "cache": {"write": 0, "read": 5632}}, {"total": 13045, "input": 7158, "output": 234, "reasoning": 21, "cache": {"write": 0, "read": 5632}}, {"total": 18969, "input": 6023, "output": 253, "reasoning": 917, "cache": {"write": 0, "read": 11776}}, {"total": 20603, "input": 3529, "output": 133, "reasoning": 45, "cache": {"write": 0, "read": 16896}}, {"total": 24505, "input": 3288, "output": 170, "reasoning": 1079, "cache": {"write": 0, "read": 19968}}, {"total": 27573, "input": 1586, "output": 2947, "reasoning": 0, "cache": {"write": 0, "read": 23040}}, {"total": 27694, "input": 3533, "output": 89, "reasoning": 8, "cache": {"write": 0, "read": 24064}}, {"total": 28094, "input": 679, "output": 279, "reasoning": 0, "cache": {"write": 0, "read": 27136}}, {"total": 28331, "input": 1028, "output": 37, "reasoning": 130, "cache": {"write": 0, "read": 27136}}, {"total": 28576, "input": 1209, "output": 231, "reasoning": 0, "cache": {"write": 0, "read": 27136}}, {"total": 29149, "input": 715, "output": 259, "reasoning": 15, "cache": {"write": 0, "read": 28160}}, {"total": 33644, "input": 3323, "output": 520, "reasoning": 1641, "cache": {"write": 0, "read": 28160}}, {"total": 33933, "input": 2436, "output": 231, "reasoning": 34, "cache": {"write": 0, "read": 31232}}, {"total": 35554, "input": 832, "output": 595, "reasoning": 847, "cache": {"write": 0, "read": 33280}}, {"total": 35815, "input": 2298, "output": 231, "reasoning": 6, "cache": {"write": 0, "read": 33280}}, {"total": 37077, "input": 665, "output": 568, "reasoning": 516, "cache": {"write": 0, "read": 35328}}, {"total": 37291, "input": 1773, "output": 178, "reasoning": 12, "cache": {"write": 0, "read": 35328}}, {"total": 37444, "input": 989, "output": 33, "reasoning": 70, "cache": {"write": 0, "read": 36352}}, {"total": 38779, "input": 2141, "output": 80, "reasoning": 206, "cache": {"write": 0, "read": 36352}}, {"total": 38979, "input": 403, "output": 176, "reasoning": 0, "cache": {"write": 0, "read": 38400}}, {"total": 39255, "input": 629, "output": 81, "reasoning": 145, "cache": {"write": 0, "read": 38400}}, {"total": 39333, "input": 879, "output": 54, "reasoning": 0, "cache": {"write": 0, "read": 38400}}, {"total": 39589, "input": 950, "output": 231, "reasoning": 8, "cache": {"write": 0, "read": 38400}}, {"total": 41023, "input": 1365, "output": 216, "reasoning": 1042, "cache": {"write": 0, "read": 38400}}, {"total": 42478, "input": 1616, "output": 272, "reasoning": 1166, "cache": {"write": 0, "read": 39424}}, {"total": 42611, "input": 2077, "output": 86, "reasoning": 0, "cache": {"write": 0, "read": 40448}}, {"total": 42857, "input": 1259, "output": 126, "reasoning": 0, "cache": {"write": 0, "read": 41472}}], "token_basis": "vendor_split", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "tokens_fresh": 70485, "turns": 28, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_opencode_30258_d43930ce", "-v", "/dev/openbench/.bench-tmp/bench_opencode_terminal-bench_schemelike-metacircular-eval_5w8c0_b8:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_bd5115s5.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-v", "/.local/share/opencode/auth.json:/bench/auth/.local/share/opencode/auth.json:ro", "openbench-harness@sha256:", "python3", "/bench/entry.py", "opencode", "gpt-5.6-sol", "2400"], "adapter_cmd": ["opencode", "run", "--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784483390.1535668, "sha256": ""}, "eval.scm": {"bytes": 12256, "mtime": 1784483338.6875782, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, "sha256": ""}, "test/06-interactive-io.scm": {"bytes": 992, "mtime": 1784160991.2214541, "sha256": ""}, "test/08-progn-sequencing.scm": {"bytes": 1067, "mtime": 1784160991.2214954, "sha256": ""}, "test/09-mutual-recursion.scm": {"bytes": 895, "mtime": 1784160991.2215376, "sha256": ""}, "test/10-advanced-features.scm": {"bytes": 1945, "mtime": 1784160991.2215784, "sha256": ""}, "test/README.md": {"bytes": 2108, "mtime": 1784160991.2216206, "sha256": ""}, "test/accumulator_patterns.scm": {"bytes": 1410, "mtime": 1784160991.2216601, "sha256": ""}, "test/binary_tree.scm": {"bytes": 1311, "mtime": 1784160991.2217, "sha256": ""}, "test/calculator.scm": {"bytes": 341, "mtime": 1784160991.2217395, "sha256": ""}, "test/church_numerals.scm": {"bytes": 1179, "mtime": 1784160991.2217798, "sha256": ""}, "test/closures.scm": {"bytes": 904, "mtime": 1784160991.221818, "sha256": ""}, "test/continuation_passing.scm": {"bytes": 1104, "mtime": 1784160991.2218595, "sha256": ""}, "test/currying.scm": {"bytes": 1578, "mtime": 1784160991.2218997, "sha256": ""}, "test/filter_operations.scm": {"bytes": 719, "mtime": 1784160991.2219415, "sha256": ""}, "test/fold_operations.scm": {"bytes": 800, "mtime": 1784160991.2219827, "sha256": ""}, "test/function_composition.scm": {"bytes": 1397, "mtime": 1784160991.2220268, "sha256": ""}, "test/lazy_evaluation.scm": {"bytes": 1279, "mtime": 1784160991.2220654, "sha256": ""}, "test/list_operations.scm": {"bytes": 1683, "mtime": 1784160991.222102, "sha256": ""}, "test/map_operations.scm": {"bytes": 637, "mtime": 1784160991.222143, "sha256": ""}, "test/memoization.scm": {"bytes": 1694, "mtime": 1784160991.2221804, "sha256": ""}, "test/mutual_recursion.scm": {"bytes": 1892, "mtime": 1784160991.222222, "sha256": ""}, "test/nested_defines.scm": {"bytes": 1133, "mtime": 1784160991.2222624, "sha256": ""}, "test/oeis_sequences.scm": {"bytes": 2009, "mtime": 1784160991.2223027, "sha256": ""}, "test/oeis_sequences2.scm": 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"-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784483764.4731386, "sha256": ""}, "eval.scm": {"bytes": 8929, "mtime": 1784485291.3617256, "sha256": ""}, "interp.py": {"bytes": 17576, "mtime": 1784485266.3227017, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 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"--dir", "/work", "-m", "openai/gpt-5.6-sol", "--variant", "medium", "--auto", "--format", "json", "--title", "openbench", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784486681.9824529, "sha256": ""}, "eval.scm": {"bytes": 9583, "mtime": 1784486656.211326, "sha256": ""}, "handler-test.txt": {"bytes": 23, "mtime": 1784486686.417043, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, 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"mtime": 1784160991.2209766, "sha256": ""}, "shadow_test/oeis_sequences3.scm": {"bytes": 2565, "mtime": 1784160991.2210133, "sha256": ""}, "shadow_test/recursive_structures.scm": {"bytes": 2994, "mtime": 1784160991.221053, "sha256": ""}, "shadow_test/test_read.scm": {"bytes": 90, "mtime": 1784160991.22109, "sha256": ""}, "shadow_test/variadic_functions.scm": {"bytes": 2434, "mtime": 1784160991.221138, "sha256": ""}, "shadow_test/y_combinator.scm": {"bytes": 877, "mtime": 1784160991.2211754, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial1", "ts_iso": "2026-07-20T01:17:41", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 47.729, "t_env_setup_s": 0.098, "t_agent_s": 47.574, "t_checker_s": 0.234, "tokens": 90362, "tokens_input_uncached": 10762, "tokens_cache_read": 77824, "tokens_cache_write": null, "tokens_output": 1776, "tokens_reasoning": 475, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 11428, "tokens_proxy_cache_read": 77824, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1800, "tokens_proxy_reasoning": 475, "tokens_proxy_calls": 11, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 12538, "turns": 10, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_8ba3e334", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_db-wal-recovery_0wrlyv0r:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_2g023_wy.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=JPGaR8aIrkNO8ZMUXSNl8iOo", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_7u3czyba:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: main.db-wal does not have a valid SQLite WAL header\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784524692.6095257, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784524701.105395, "sha256": ""}, "main.db-wal": {"bytes": 0, "mtime": 1784524701.1029828, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784524701.1063058, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial2", "ts_iso": "2026-07-20T01:18:29", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 53.258, "t_env_setup_s": 0.092, "t_agent_s": 53.11, "t_checker_s": 0.231, "tokens": 91587, "tokens_input_uncached": 12832, "tokens_cache_read": 76800, "tokens_cache_write": null, "tokens_output": 1955, "tokens_reasoning": 412, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 13498, "tokens_proxy_cache_read": 76800, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 1980, "tokens_proxy_reasoning": 412, "tokens_proxy_calls": 11, "sampling_observed": [{"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}, {"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 14787, "turns": 10, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_59a42a80", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_db-wal-recovery_l2sxa50q:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_wp8rjwy3.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=87ztClAVf5QEBzwwsIQiphwn", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_r8fdg_id:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784524757.5853767, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784524742.335546, "sha256": ""}, "recovered.json": {"bytes": 472, "mtime": 1784524750.272747, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial3", "ts_iso": "2026-07-20T01:19:23", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 141.432, "t_env_setup_s": 0.093, "t_agent_s": 141.275, "t_checker_s": 0.23, "tokens": 394397, "tokens_input_uncached": 34030, "tokens_cache_read": 354816, "tokens_cache_write": null, "tokens_output": 5551, "tokens_reasoning": 2171, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 34696, "tokens_proxy_cache_read": 354816, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5575, "tokens_proxy_reasoning": 2171, "tokens_proxy_calls": 22, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 39581, "turns": 21, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_c865afec", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_db-wal-recovery_jwiuv37o:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_mv5zekhm.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=qjOAvdj1R20FImmakPWB74Pp", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_nsi0igmn:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: recovered rows do not match the checker-owned oracle\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784524894.5903738, "sha256": ""}, "main.db-wal": {"bytes": 4152, "mtime": 1784524894.5915701, "sha256": ""}, "recovered.json": {"bytes": 472, "mtime": 1784524879.145364, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/extract-elf:gpt-5.6-sol:trial1", "ts_iso": "2026-07-20T01:21:45", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 157.579, "t_env_setup_s": 0.088, "t_agent_s": 157.43, "t_checker_s": 0.293, "tokens": 176088, "tokens_input_uncached": 16033, "tokens_cache_read": 152576, "tokens_cache_write": null, "tokens_output": 7479, "tokens_reasoning": 3855, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 16671, "tokens_proxy_cache_read": 152576, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 7503, "tokens_proxy_reasoning": 3855, "tokens_proxy_calls": 15, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 23512, "turns": 14, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_dec7367e", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_extract-elf_qpoo_8z0:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_4mcb6y51.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=jPqJXRnsoNKFRo6aiGK4eb-5", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_2wdb6i3p:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 4330, "mtime": 1784525003.4300091, "sha256": ""}, "out.json": {"bytes": 9960, "mtime": 1784525024.4675567, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/extract-elf:gpt-5.6-sol:trial2", "ts_iso": "2026-07-20T01:24:23", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 134.939, "t_env_setup_s": 0.088, "t_agent_s": 134.799, "t_checker_s": 0.288, "tokens": 154682, "tokens_input_uncached": 16001, "tokens_cache_read": 132608, "tokens_cache_write": null, "tokens_output": 6073, "tokens_reasoning": 2828, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 16639, "tokens_proxy_cache_read": 132608, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6097, "tokens_proxy_reasoning": 2828, "tokens_proxy_calls": 14, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 22074, "turns": 13, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_46da1b1e", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_extract-elf_aa3vu7sw:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_x71hkcwo.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=ZXyTtoqsbQTsqpNQcNKPDpkg", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_2dybaklh:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3669, "mtime": 1784525155.7368872, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/extract-elf:gpt-5.6-sol:trial3", "ts_iso": "2026-07-20T01:26:38", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 164.141, "t_env_setup_s": 0.086, "t_agent_s": 163.993, "t_checker_s": 0.284, "tokens": 231765, "tokens_input_uncached": 22203, "tokens_cache_read": 202240, "tokens_cache_write": null, "tokens_output": 7322, "tokens_reasoning": 3385, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 22841, "tokens_proxy_cache_read": 202240, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 7347, "tokens_proxy_reasoning": 3385, "tokens_proxy_calls": 18, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 29525, "turns": 17, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_27d7059c", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_extract-elf_hmv5lkb6:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_0ixd793h.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=ZlLwF4dFwYDtC70EInTXFciU", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_dc9tpiju:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3734, "mtime": 1784525319.9669654, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial1", "ts_iso": "2026-07-20T01:29:23", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 127.569, "t_env_setup_s": 0.087, "t_agent_s": 127.429, "t_checker_s": 0.588, "tokens": 102197, "tokens_input_uncached": 10714, "tokens_cache_read": 86016, "tokens_cache_write": null, "tokens_output": 5467, "tokens_reasoning": 3005, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 11370, "tokens_proxy_cache_read": 86016, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5496, "tokens_proxy_reasoning": 3005, "tokens_proxy_calls": 11, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 16181, "turns": 10, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_4847250e", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_feal-differential-cryptanalysis_3pw55_30:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_gp62e_3a.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=LIUBMeI4MXOMx6Zl33a4_jAM", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_s75dcmpw:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 2934, "mtime": 1784525462.6587749, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial2", "ts_iso": "2026-07-20T01:31:31", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 255.515, "t_env_setup_s": 0.087, "t_agent_s": 255.362, "t_checker_s": 0.644, "tokens": 144397, "tokens_input_uncached": 16042, "tokens_cache_read": 122368, "tokens_cache_write": null, "tokens_output": 5987, "tokens_reasoning": 3426, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 16698, "tokens_proxy_cache_read": 122368, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6016, "tokens_proxy_reasoning": 3426, "tokens_proxy_calls": 14, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 22029, "turns": 13, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_2c6e3e0e", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_feal-differential-cryptanalysis_ccjba09r:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_s517s82v.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=WBsxVjmRFllOfGqrMD5U8prG", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_ofcm_ldt:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 2892, "mtime": 1784525721.5453992, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial3", "ts_iso": "2026-07-20T01:35:47", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 263.444, "t_env_setup_s": 0.09, "t_agent_s": 263.305, "t_checker_s": 0.526, "tokens": 128698, "tokens_input_uncached": 17791, "tokens_cache_read": 105472, "tokens_cache_write": null, "tokens_output": 5435, "tokens_reasoning": 2865, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 18447, "tokens_proxy_cache_read": 105472, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5464, "tokens_proxy_reasoning": 2865, "tokens_proxy_calls": 13, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 23226, "turns": 12, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_a31ebe90", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_feal-differential-cryptanalysis_n_nokkvw:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_iajlx_2i.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=YKnrdZyva-yyQPE4NM3GSVY5", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_w0dyb9ok:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 3002, "mtime": 1784525849.0562496, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/gcode-to-text:gpt-5.6-sol:trial1", "ts_iso": "2026-07-20T01:40:11", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 158.074, "t_env_setup_s": 0.09, "t_agent_s": 157.936, "t_checker_s": 0.234, "tokens": 343502, "tokens_input_uncached": 69370, "tokens_cache_read": 267776, "tokens_cache_write": null, "tokens_output": 6356, "tokens_reasoning": 1483, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 69905, "tokens_proxy_cache_read": 267776, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6382, "tokens_proxy_reasoning": 1483, "tokens_proxy_calls": 18, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 75726, "turns": 17, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_8fbc7d4e", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_gcode-to-text_3gp49j0n:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_t3iseos9.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=PhqWqbL_7WRM80b5PtAG6AQi", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_qkha0oav:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784526167.2709627, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/gcode-to-text:gpt-5.6-sol:trial2", "ts_iso": "2026-07-20T01:42:50", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 311.293, "t_env_setup_s": 0.088, "t_agent_s": 311.157, "t_checker_s": 0.234, "tokens": 344993, "tokens_input_uncached": 56043, "tokens_cache_read": 283648, "tokens_cache_write": null, "tokens_output": 5302, "tokens_reasoning": 1237, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 56578, "tokens_proxy_cache_read": 283648, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 5328, "tokens_proxy_reasoning": 1237, "tokens_proxy_calls": 21, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 61345, "turns": 20, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_4f1701c1", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_gcode-to-text_qws31bo4:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_90qvkobn.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=iyd4nipvQSrU4qI_nfqIWEqG", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_dij1hwq_:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784526473.7670732, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/gcode-to-text:gpt-5.6-sol:trial3", "ts_iso": "2026-07-20T01:48:02", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 269.777, "t_env_setup_s": 0.083, "t_agent_s": 269.637, "t_checker_s": 0.233, "tokens": 946259, "tokens_input_uncached": 75759, "tokens_cache_read": 861184, "tokens_cache_write": null, "tokens_output": 9316, "tokens_reasoning": 2913, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 76294, "tokens_proxy_cache_read": 861184, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 9342, "tokens_proxy_reasoning": 2913, "tokens_proxy_calls": 35, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 85075, "turns": 34, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_2be55a9c", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_gcode-to-text_gwosy70s:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_y33kp1_j.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=KZ5JecqDhmosFxi5xUorKbvS", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_tf1g_3cq:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784526744.4526849, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial1", "ts_iso": "2026-07-20T01:52:32", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 892.222, "t_env_setup_s": 0.093, "t_agent_s": 892.072, "t_checker_s": 0.232, "tokens": 401080, "tokens_input_uncached": 37249, "tokens_cache_read": 355840, "tokens_cache_write": null, "tokens_output": 7991, "tokens_reasoning": 3662, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 38549, "tokens_proxy_cache_read": 355840, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 8019, "tokens_proxy_reasoning": 3662, "tokens_proxy_calls": 18, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 45240, "turns": 17, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_803acaf5", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_llm-inference-batching-scheduler_j2g431pl:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_xnnr66h9.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=ZHabePbX3NTUQ_DYFid_Tf7X", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_ppth4g4x:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784527600.4934442, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784527612.8735626, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial2", "ts_iso": "2026-07-20T02:07:24", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 188.444, "t_env_setup_s": 0.092, "t_agent_s": 188.294, "t_checker_s": 0.228, "tokens": 234541, "tokens_input_uncached": 27387, "tokens_cache_read": 200192, "tokens_cache_write": null, "tokens_output": 6962, "tokens_reasoning": 3391, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 28687, "tokens_proxy_cache_read": 200192, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 6990, "tokens_proxy_reasoning": 3391, "tokens_proxy_calls": 16, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 34349, "turns": 15, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_81f8b0ed", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_llm-inference-batching-scheduler_r_n5j_rg:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_08ydi3l1.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=mnjEfasnUCfM1zQ9VDMwlfLv", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_ja4ccirh:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/optimized_packer.py": {"bytes": 2531, "mtime": 1784527782.0098758, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88122, "mtime": 1784527786.6110122, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784527786.6170862, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial3", "ts_iso": "2026-07-20T02:10:33", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 266.903, "t_env_setup_s": 0.088, "t_agent_s": 266.754, "t_checker_s": 0.237, "tokens": 427257, "tokens_input_uncached": 38017, "tokens_cache_read": 379904, "tokens_cache_write": null, "tokens_output": 9336, "tokens_reasoning": 4164, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 39317, "tokens_proxy_cache_read": 379904, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 9364, "tokens_proxy_reasoning": 4164, "tokens_proxy_calls": 17, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 47353, "turns": 16, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_55b5fd80", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_llm-inference-batching-scheduler_x9bwnf2w:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_d7mrl6n0.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=psESpM1eDt7ogZXSPC_Bi2xF", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_xz_cx3ya:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 95238, "mtime": 1784528058.5956502, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 96679, "mtime": 1784528070.7848344, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/raman-fitting:gpt-5.6-sol:trial1", "ts_iso": "2026-07-20T02:15:00", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 231.432, "t_env_setup_s": 0.087, "t_agent_s": 231.301, "t_checker_s": 0.231, "tokens": 300961, "tokens_input_uncached": 45547, "tokens_cache_read": 245760, "tokens_cache_write": null, "tokens_output": 9654, "tokens_reasoning": 5334, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 46196, "tokens_proxy_cache_read": 245760, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 9682, "tokens_proxy_reasoning": 5334, "tokens_proxy_calls": 24, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 55201, "turns": 23, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_ac74094e", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_raman-fitting_ccumge_6:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_awqndlsi.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=CMH_dfUcYvxhFm5aWhW2lEaE", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_7blpwbga:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 239, "mtime": 1784528320.193594, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/raman-fitting:gpt-5.6-sol:trial2", "ts_iso": "2026-07-20T02:18:52", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 215.866, "t_env_setup_s": 0.088, "t_agent_s": 215.715, "t_checker_s": 0.225, "tokens": 348347, "tokens_input_uncached": 34761, "tokens_cache_read": 304640, "tokens_cache_write": null, "tokens_output": 8946, "tokens_reasoning": 5165, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 35410, "tokens_proxy_cache_read": 304640, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 8974, "tokens_proxy_reasoning": 5165, "tokens_proxy_calls": 22, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 43707, "turns": 21, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_31c32ba3", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_raman-fitting_aj6p5jix:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_dp6giwmx.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=by1nc9udRQAzeC_wE1Tysldn", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_088vvex3:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 239, "mtime": 1784528524.2345524, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/raman-fitting:gpt-5.6-sol:trial3", "ts_iso": "2026-07-20T02:22:28", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 166.013, "t_env_setup_s": 0.088, "t_agent_s": 165.861, "t_checker_s": 0.235, "tokens": 235048, "tokens_input_uncached": 19965, "tokens_cache_read": 207872, "tokens_cache_write": null, "tokens_output": 7211, "tokens_reasoning": 3660, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 20614, "tokens_proxy_cache_read": 207872, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 7239, "tokens_proxy_reasoning": 3660, "tokens_proxy_calls": 21, "sampling_observed": [{"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}, {"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 27176, "turns": 20, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_4a771cfb", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_raman-fitting_j4piusxo:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_0s8w8beu.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=FilaR2IVCSmRgPA236W2x_Z0", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_7lp15r0x:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 1, "exec_mode": "docker", "score": 0.0, "harness_version": null, "harness_version_source": "container", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: 2D parameters outside tolerance: offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 235, "mtime": 1784528702.2365963, "sha256": ""}}, "image_digest": "openbench-harness@sha256:", "candidate_provenance": null, "version_drift": false, "timeout_s": 2400} {"run_id": "grokbuild:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial1", "ts_iso": "2026-07-20T02:25:15", "harness": "grokbuild", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 1209.417, "t_env_setup_s": 0.108, "t_agent_s": 1209.262, "t_checker_s": 37.974, "tokens": 907311, "tokens_input_uncached": 48850, "tokens_cache_read": 845824, "tokens_cache_write": null, "tokens_output": 12637, "tokens_reasoning": 5110, "usage_raw": null, "token_basis": null, "tokens_proxy_input_uncached": 49744, "tokens_proxy_cache_read": 845824, "tokens_proxy_cache_write": 0, "tokens_proxy_output": 12665, "tokens_proxy_reasoning": 5110, "tokens_proxy_calls": 33, "sampling_observed": [{"max_tokens": 100, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}, "temperature": 1.0}, {"max_tokens": 65536, "model": "gpt-5.6-sol", "stream": true, "stream_options": {"include_usage": true}}], "token_basis_proxy": "proxy_measured", "tokens_fresh": 61487, "turns": 32, "cmd": {"docker": ["docker", "run", "--rm", "--cpus", "4", "--name", "openbench_grokbuild_59747_be364cf0", "-v", "/dev/openbench/.bench-tmp/bench_grokbuild_terminal-bench_schemelike-metacircular-eval_4jdx4kca:/work", "-v", "/dev/openbench/bench/adapters:/bench/adapters:ro", "-v", "/dev/openbench/bench/entry.py:/bench/entry.py:ro", "-v", "/dev/openbench/bench/auth_persist.py:/bench/auth_persist.py:ro", "-v", "/dev/openbench/.bench-tmp/bench_instr_pad72dv4.txt:/bench/instruction.txt:ro", "-w", "/work", "-e", "HOME=/root", "-e", "CLIPROXYAPI_API_KEY", "-e", "OPENBENCH_PROXY=1", "-e", "OPENBENCH_PROXY_BASE_URL=http://host.docker.internal:49656", "-e", "OPENBENCH_PROXY_CELL_TOKEN=kHeWRl-CB3mdK7G3GoKSn_es", "-v", "/.openbench/grok-container-auth/auth.json:/bench/auth/.grok/auth.json:ro", "-v", "/dev/openbench/.bench-tmp/bench_auth_return_d8y7pn1g:/bench/auth-return:rw", "-e", "BENCH_AUTH_PERSIST_HARNESS=grokbuild", "--add-host", "host.docker.internal:host-gateway", "openbench-harness@sha256:", "python3", "/bench/entry.py", "grokbuild", "gpt-5.6-sol", "2400"], "adapter_cmd": ["/usr/local/bin/grok", "--no-auto-update", "-p", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784529898.8751936, "sha256": ""}, "eval.scm": {"bytes": 10265, "mtime": 1784529887.836204, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, "sha256": ""}, "test/06-interactive-io.scm": {"bytes": 992, "mtime": 1784160991.2214541, "sha256": ""}, "test/08-progn-sequencing.scm": {"bytes": 1067, "mtime": 1784160991.2214954, "sha256": ""}, "test/09-mutual-recursion.scm": {"bytes": 895, "mtime": 1784160991.2215376, "sha256": ""}, "test/10-advanced-features.scm": {"bytes": 1945, "mtime": 1784160991.2215784, "sha256": ""}, "test/README.md": {"bytes": 2108, "mtime": 1784160991.2216206, "sha256": ""}, "test/accumulator_patterns.scm": {"bytes": 1410, "mtime": 1784160991.2216601, "sha256": ""}, "test/binary_tree.scm": {"bytes": 1311, "mtime": 1784160991.2217, "sha256": ""}, "test/calculator.scm": {"bytes": 341, "mtime": 1784160991.2217395, "sha256": ""}, "test/church_numerals.scm": {"bytes": 1179, "mtime": 1784160991.2217798, "sha256": ""}, "test/closures.scm": {"bytes": 904, "mtime": 1784160991.221818, "sha256": ""}, "test/continuation_passing.scm": {"bytes": 1104, "mtime": 1784160991.2218595, "sha256": ""}, "test/currying.scm": {"bytes": 1578, "mtime": 1784160991.2218997, "sha256": ""}, "test/filter_operations.scm": {"bytes": 719, "mtime": 1784160991.2219415, "sha256": ""}, "test/fold_operations.scm": {"bytes": 800, "mtime": 1784160991.2219827, "sha256": ""}, "test/function_composition.scm": {"bytes": 1397, "mtime": 1784160991.2220268, "sha256": ""}, "test/lazy_evaluation.scm": {"bytes": 1279, "mtime": 1784160991.2220654, "sha256": ""}, "test/list_operations.scm": {"bytes": 1683, "mtime": 1784160991.222102, "sha256": ""}, "test/map_operations.scm": {"bytes": 637, "mtime": 1784160991.222143, "sha256": ""}, "test/memoization.scm": {"bytes": 1694, "mtime": 1784160991.2221804, "sha256": ""}, "test/mutual_recursion.scm": {"bytes": 1892, "mtime": 1784160991.222222, "sha256": 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"shadow_test/04-higher-order.scm": {"bytes": 988, "mtime": 1784160991.2200537, "sha256": ""}, "shadow_test/05-simple-io.scm": {"bytes": 947, "mtime": 1784160991.2200944, "sha256": ""}, "shadow_test/06-interactive-io.scm": {"bytes": 1032, "mtime": 1784160991.220135, "sha256": ""}, "shadow_test/08-progn-sequencing.scm": {"bytes": 1087, "mtime": 1784160991.220178, "sha256": ""}, "shadow_test/09-mutual-recursion.scm": {"bytes": 986, "mtime": 1784160991.2202172, "sha256": ""}, "shadow_test/10-advanced-features.scm": {"bytes": 2052, "mtime": 1784160991.220261, "sha256": ""}, "shadow_test/accumulator_patterns.scm": {"bytes": 1427, "mtime": 1784160991.220304, "sha256": ""}, "shadow_test/binary_tree.scm": {"bytes": 1342, "mtime": 1784160991.2203417, "sha256": ""}, "shadow_test/church_numerals.scm": {"bytes": 1225, "mtime": 1784160991.220378, "sha256": ""}, "shadow_test/closures.scm": {"bytes": 911, "mtime": 1784160991.2204163, "sha256": ""}, "shadow_test/continuation_passing.scm": {"bytes": 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"--no-auto-update", "-p", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784530272.193961, "sha256": ""}, "eval.scm": {"bytes": 9480, "mtime": 1784531105.380874, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, 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"--no-auto-update", "-p", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n", "--model", "gpt-5.6-sol", "--output-format", "streaming-json", "--effort", "medium", "--reasoning-effort", "medium", "--always-approve", "--cwd", "/work"]}, "checker_exit": 0, "exec_mode": "docker", "score": 1.0, "harness_version": null, "harness_version_source": "container", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784532599.3206754, "sha256": ""}, "eval.scm": {"bytes": 9669, "mtime": 1784532550.829912, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, 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"wall_time_s": 150.015, "t_env_setup_s": 0.005, "t_agent_s": 150.015, "t_checker_s": 0.234, "tokens": 33551, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 435896, "total_completion_tokens": 6354, "total_cached_tokens": 408699, "total_steps": 29, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 22, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_ozguh67k.json", "--", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3323, "mtime": 1784636609.3678272, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1309, "mtime": 1784636585.7835855, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:add-feature:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T08:23:52", "harness": "devin", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 111.221, "t_env_setup_s": 0.004, "t_agent_s": 111.221, "t_checker_s": 0.233, "tokens": 31386, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 311704, "total_completion_tokens": 5724, "total_cached_tokens": 286042, "total_steps": 25, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 18, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_rewso72u.json", "--", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3153, "mtime": 1784636697.2671824, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1431, "mtime": 1784636717.703318, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:add-feature:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T08:25:43", "harness": "devin", "model": "gpt-5.6-sol", "task": "add-feature", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 117.454, "t_env_setup_s": 0.004, "t_agent_s": 117.454, "t_checker_s": 0.234, "tokens": 28575, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 301878, "total_completion_tokens": 5452, "total_cached_tokens": 278755, "total_steps": 24, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 17, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_6yvjsei2.json", "--", "# Add `@include` support to miniconf\n\n`miniconf` is a small INI-style configuration parser (see `miniconf/`). It\nsupports comments, `[sections]`, `key = value` assignments, and typed values.\nIts existing test suite is under `tests/` and passes.\n\nWe need configuration files to be able to pull in other files. Add support for\nan `@include` directive to the file loader.\n\n## Directive syntax\n\nA line whose first whitespace-separated token is `@include`, followed by a\npath, includes another configuration file at that point:\n\n```\n@include logging.conf\n```\n\n## Required semantics\n\n- **Inline expansion.** An `@include` line is replaced by the full contents of\n the referenced file, in place, as if those lines had been written where the\n directive appears. Parsing (sections, keys, comments, value coercion) then\n proceeds over the expanded text exactly as today.\n- **Relative paths.** The include path is resolved relative to the directory of\n the file that contains the directive (not the process working directory).\n- **Ordering / override.** Because expansion is positional, a key assigned\n *after* an `@include` overrides a value that the included file set for the\n same key, and a key assigned before it is overridden by the include. This\n follows from normal \"later assignment wins\" behavior once the file is\n expanded.\n- **Nesting.** An included file may itself contain `@include` directives; expand\n them recursively.\n- **Missing file.** If an include target does not exist, raise\n `miniconf.errors.ConfigError`.\n- **Cycles.** If a file includes itself directly or transitively (a cycle),\n raise `miniconf.errors.ConfigError` rather than looping forever.\n\nOnly `miniconf.loader.load(path)` needs to handle includes. The behavior of\nevery existing function, and of files that contain no `@include` directives,\nmust not change \u2014 the current test suite must still pass.\n\nAn example pair of files is provided in `examples/` (`service.conf` includes\n`logging.conf`) to try your implementation against.\n\n## Done when\n\n`miniconf.loader.load(path)` expands `@include` directives per the semantics\nabove, existing behavior is unchanged, and the existing test suite still\npasses.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "feature 6/6, regression-groups 4/4\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4624026, "sha256": ""}, "tests/test_config.py": {"bytes": 770, "mtime": 1783083001.4624946, "sha256": ""}, "tests/test_loader.py": {"bytes": 3363, "mtime": 1784636792.9585268, "sha256": ""}, "tests/test_parser.py": {"bytes": 1203, "mtime": 1783083001.4626524, "sha256": ""}, "tests/test_values.py": {"bytes": 507, "mtime": 1783083001.462728, "sha256": ""}, "miniconf/__init__.py": {"bytes": 226, "mtime": 1783083001.4618776, "sha256": ""}, "miniconf/config.py": {"bytes": 1265, "mtime": 1783083001.4619687, "sha256": ""}, "miniconf/errors.py": {"bytes": 140, "mtime": 1783083001.4620552, "sha256": ""}, "miniconf/loader.py": {"bytes": 1512, "mtime": 1784636826.656435, "sha256": ""}, "miniconf/parser.py": {"bytes": 1538, "mtime": 1783083001.4622242, "sha256": ""}, "miniconf/values.py": {"bytes": 771, "mtime": 1783083001.4623086, "sha256": ""}, "examples/logging.conf": {"bytes": 86, "mtime": 1783083001.4616504, "sha256": ""}, "examples/service.conf": {"bytes": 120, "mtime": 1783083001.461746, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:build-a-cli:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T08:27:41", "harness": "devin", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 37.184, "t_env_setup_s": 0.001, "t_agent_s": 37.184, "t_checker_s": 0.234, "tokens": 14549, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 103672, "total_completion_tokens": 1745, "total_cached_tokens": 90868, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_qqa55yza.json", "--", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 672, "mtime": 1784636880.7575693, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:build-a-cli:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T08:28:18", "harness": "devin", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 58.967, "t_env_setup_s": 0.001, "t_agent_s": 58.967, "t_checker_s": 0.231, "tokens": 15434, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 116238, "total_completion_tokens": 2366, "total_cached_tokens": 103170, "total_steps": 17, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 10, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_jkvdfklo.json", "--", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 464, "mtime": 1784636919.474732, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:build-a-cli:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T08:29:18", "harness": "devin", "model": "gpt-5.6-sol", "task": "build-a-cli", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 44.607, "t_env_setup_s": 0.001, "t_agent_s": 44.607, "t_checker_s": 0.273, "tokens": 15206, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 117735, "total_completion_tokens": 1853, "total_cached_tokens": 104382, "total_steps": 17, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 10, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_5svio7iv.json", "--", "# Build a word-frequency command-line tool\n\nWrite a Python script named `wordcount.py` in this directory. It reads a text\nfile and prints the most common words it contains.\n\n## Usage\n\n```\npython3 wordcount.py \n```\n\n- `` \u2014 path to a UTF-8 text file to analyze.\n- `` \u2014 a non-negative integer: how many of the top words to print.\n\n## What counts as a word\n\nLowercase the entire file, then treat every maximal run of the characters\n`a`\u2013`z` and `0`\u2013`9` as one word. Every other character (spaces, punctuation,\nnewlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`\ncontains the words `hello`, `hello`, `world`.\n\n## Output\n\nPrint the `N` most frequent words, one per line, as:\n\n```\n \n```\n\nthe word and the count separated by a single space, each line ending with a\nnewline.\n\nOrdering rules:\n\n- Sort by count, highest first.\n- Break ties between words with equal counts alphabetically (ascending).\n- If the file has fewer than `N` distinct words, print all of them.\n- If `N` is `0`, print nothing.\n\n## Example\n\nA sample file `sample.txt` is included. Running:\n\n```\npython3 wordcount.py sample.txt 3\n```\n\nshould print:\n\n```\nthe 3\nfox 2\nquick 2\n```\n\nDone when `wordcount.py` behaves exactly as specified above.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "All wordcount cases passed.\n", "checker_stderr": "", "checker_workspace_files": {"sample.txt": {"bytes": 60, "mtime": 1783083001.464147, "sha256": ""}, "wordcount.py": {"bytes": 767, "mtime": 1784636979.7479954, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:fix-failing-test:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T08:30:02", "harness": "devin", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 27.216, "t_env_setup_s": 0.002, "t_agent_s": 27.216, "t_checker_s": 0.237, "tokens": 13158, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 101893, "total_completion_tokens": 893, "total_cached_tokens": 89628, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_c6g5ye81.json", "--", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784637018.5607548, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:fix-failing-test:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T08:30:30", "harness": "devin", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 24.55, "t_env_setup_s": 0.002, "t_agent_s": 24.55, "t_checker_s": 0.239, "tokens": 13060, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 101548, "total_completion_tokens": 854, "total_cached_tokens": 89342, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_uszo4emx.json", "--", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784637048.83476, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:fix-failing-test:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T08:30:55", "harness": "devin", "model": "gpt-5.6-sol", "task": "fix-failing-test", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 26.323, "t_env_setup_s": 0.002, "t_agent_s": 26.323, "t_checker_s": 0.237, "tokens": 13081, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 102261, "total_completion_tokens": 827, "total_cached_tokens": 90007, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_5o1ogqcv.json", "--", "# Fix the failing calculator tests\n\nThe `calculator.py` module provides basic arithmetic helpers, and\n`test_calculator.py` contains a unit-test suite for them. One of the tests is\ncurrently failing.\n\nRun the test suite:\n\n```\npython3 -m unittest\n```\n\nFind out why it fails and fix the bug in `calculator.py` so that the whole\nsuite passes. Do not change the tests \u2014 they describe the correct behavior.\n\nDone when `python3 -m unittest` reports all tests passing with a zero exit code.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "", "checker_stderr": "test_add (test_calculator.TestCalculator.test_add) ... ok\ntest_divide (test_calculator.TestCalculator.test_divide) ... ok\ntest_multiply (test_calculator.TestCalculator.test_multiply) ... ok\ntest_subtract (test_calculator.TestCalculator.test_subtract) ... ok\n\n----------------------------------------------------------------------\nRan 4 tests in 0.000s\n\nOK\n", "checker_workspace_files": {"calculator.py": {"bytes": 147, "mtime": 1784637070.183558, "sha256": ""}, "test_calculator.py": {"bytes": 446, "mtime": 1783083001.46474, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:make-ci-green:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T08:31:21", "harness": "devin", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 53.336, "t_env_setup_s": 0.006, "t_agent_s": 53.336, "t_checker_s": 0.166, "tokens": 22194, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 160397, "total_completion_tokens": 2420, "total_cached_tokens": 140623, "total_steps": 17, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 10, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_i8vqg_g6.json", "--", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784637120.8318338, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784637120.8321583, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784637120.8323915, "sha256": ""}, "catalog/inventory.py": {"bytes": 744, "mtime": 1784637120.8325942, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784637120.8328037, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784637120.8329742, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:make-ci-green:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T08:32:15", "harness": "devin", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 50.451, "t_env_setup_s": 0.005, "t_agent_s": 50.451, "t_checker_s": 0.214, "tokens": 22559, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 161802, "total_completion_tokens": 2492, "total_cached_tokens": 141735, "total_steps": 17, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 10, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_ic9e8619.json", "--", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784637173.0786426, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784637173.078969, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784637173.0792036, "sha256": ""}, "catalog/inventory.py": {"bytes": 761, "mtime": 1784637173.079421, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784637173.0795984, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784637173.0797627, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:make-ci-green:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T08:33:05", "harness": "devin", "model": "gpt-5.6-sol", "task": "make-ci-green", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 57.78, "t_env_setup_s": 0.005, "t_agent_s": 57.78, "t_checker_s": 0.238, "tokens": 24797, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 208111, "total_completion_tokens": 2751, "total_cached_tokens": 186065, "total_steps": 19, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 12, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_zwfnmxyt.json", "--", "# Make the test suite pass\n\n`catalog/` is a small library-catalog package with a test suite under `tests/`.\nThe suite is currently failing in several places.\n\nRun it:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code in `catalog/` until the whole suite\npasses. The tests describe the intended behavior \u2014 treat them as the\nspecification and do not modify them.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "16/16 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"tests/__init__.py": {"bytes": 0, "mtime": 1783083001.4672396, "sha256": ""}, "tests/test_books.py": {"bytes": 595, "mtime": 1783083001.4673314, "sha256": ""}, "tests/test_factory.py": {"bytes": 380, "mtime": 1783083001.4674115, "sha256": ""}, "tests/test_fees.py": {"bytes": 425, "mtime": 1783083001.467485, "sha256": ""}, "tests/test_inventory.py": {"bytes": 759, "mtime": 1783083001.4675555, "sha256": ""}, "tests/test_members.py": {"bytes": 627, "mtime": 1783083001.46763, "sha256": ""}, "tests/test_search.py": {"bytes": 751, "mtime": 1783083001.467705, "sha256": ""}, "catalog/__init__.py": {"bytes": 0, "mtime": 1783083001.4665518, "sha256": ""}, "catalog/books.py": {"bytes": 679, "mtime": 1784637222.397132, "sha256": ""}, "catalog/factory.py": {"bytes": 404, "mtime": 1784637222.3974805, "sha256": ""}, "catalog/fees.py": {"bytes": 343, "mtime": 1784637222.3977103, "sha256": ""}, "catalog/inventory.py": {"bytes": 757, "mtime": 1784637236.0363894, "sha256": ""}, "catalog/members.py": {"bytes": 536, "mtime": 1784637222.3981094, "sha256": ""}, "catalog/search.py": {"bytes": 294, "mtime": 1784637222.3982797, "sha256": ""}, "catalog/text.py": {"bytes": 153, "mtime": 1783083001.4671383, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:make-it-run:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T08:34:03", "harness": "devin", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 24.227, "t_env_setup_s": 0.002, "t_agent_s": 24.227, "t_checker_s": 0.171, "tokens": 13025, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 100803, "total_completion_tokens": 950, "total_cached_tokens": 88728, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_uhd40z2w.json", "--", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784637259.7889833, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784637259.7885213, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:make-it-run:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T08:34:28", "harness": "devin", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 29.705, "t_env_setup_s": 0.001, "t_agent_s": 29.705, "t_checker_s": 0.173, "tokens": 13261, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 101657, "total_completion_tokens": 1084, "total_cached_tokens": 89480, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_5eu59huy.json", "--", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784637288.3273697, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784637288.3268926, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:make-it-run:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T08:34:58", "harness": "devin", "model": "gpt-5.6-sol", "task": "make-it-run", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 27.903, "t_env_setup_s": 0.001, "t_agent_s": 27.903, "t_checker_s": 0.17, "tokens": 13357, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 102214, "total_completion_tokens": 1122, "total_cached_tokens": 89979, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_v2db69b4.json", "--", "# Get the greeting program running\n\nThis small project is supposed to run with:\n\n```\npython3 main.py\n```\n\nand print exactly:\n\n```\nHello, world!\n```\n\nRight now it does not run \u2014 starting it raises errors. Fix the code so the\nprogram runs cleanly and prints that exact line.\n\nDone when `python3 main.py` exits successfully and prints `Hello, world!`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"greeter.py": {"bytes": 51, "mtime": 1784637316.2660096, "sha256": ""}, "main.py": {"bytes": 106, "mtime": 1784637316.265597, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:misleading-error:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T08:35:26", "harness": "devin", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 33.852, "t_env_setup_s": 0.003, "t_agent_s": 33.852, "t_checker_s": 0.173, "tokens": 16511, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 125068, "total_completion_tokens": 1306, "total_cached_tokens": 109863, "total_steps": 17, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 10, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_vihravqe.json", "--", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784637346.2306805, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:misleading-error:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T08:36:00", "harness": "devin", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 30.019, "t_env_setup_s": 0.003, "t_agent_s": 30.019, "t_checker_s": 0.171, "tokens": 14345, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 108648, "total_completion_tokens": 1135, "total_cached_tokens": 95438, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_zhhwx4p0.json", "--", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784637380.9848309, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:misleading-error:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T08:36:30", "harness": "devin", "model": "gpt-5.6-sol", "task": "misleading-error", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 62.208, "t_env_setup_s": 0.002, "t_agent_s": 62.208, "t_checker_s": 0.172, "tokens": 16868, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 140409, "total_completion_tokens": 1338, "total_cached_tokens": 124879, "total_steps": 18, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 11, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_xjxbt1vn.json", "--", "# Fix the crashing report script\n\nRunning the app fails:\n\n```\n$ python3 main.py\nTraceback (most recent call last):\n File \"main.py\", line 12, in \n main()\n File \"main.py\", line 7, in main\n total = compute_total(COUNT)\n File \"app/report.py\", line 9, in compute_total\n return rate * count\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\nTrack down why this happens and fix it so the program runs cleanly. When it\nworks, `python3 main.py` should print exactly:\n\n```\nTotal: 5.0\n```\n\nDone when `python3 main.py` exits successfully and prints that line.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "main.py ran and printed the expected line.\n", "checker_stderr": "", "checker_workspace_files": {"main.py": {"bytes": 173, "mtime": 1783083001.469145, "sha256": ""}, "settings.json": {"bytes": 59, "mtime": 1784637419.104853, "sha256": ""}, "app/__init__.py": {"bytes": 0, "mtime": 1783083001.4687982, "sha256": ""}, "app/config.py": {"bytes": 290, "mtime": 1783083001.4688964, "sha256": ""}, "app/pricing.py": {"bytes": 196, "mtime": 1783083001.4689794, "sha256": ""}, "app/report.py": {"bytes": 201, "mtime": 1783083001.469062, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:taskflow:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T08:37:32", "harness": "devin", "model": "gpt-5.6-sol", "task": "taskflow", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 71.713, "t_env_setup_s": 0.014, "t_agent_s": 71.713, "t_checker_s": 0.239, "tokens": 57327, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 466214, "total_completion_tokens": 3237, "total_cached_tokens": 412124, "total_steps": 20, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 13, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_m7wwdkjk.json", "--", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, 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"--model", "gpt-5-6-sol-medium", "--export", "/devin_export_ta1ncqns.json", "--", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, 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"--model", "gpt-5-6-sol-medium", "--export", "/devin_export_ia1w7s04.json", "--", "# Fix the failing tests in `taskflow`\n\n`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of\ninterdependent tasks to completion on a virtual clock, with priority\nscheduling, retries with backoff, resource-limited concurrency, a per-job state\nmachine, an event bus, and an event-sourced run history. The package has a\n`tests/` suite that describes how all of this is supposed to behave.\n\nRight now the suite is failing in several places. Run it from this directory:\n\n```\npython3 -m unittest\n```\n\nWork through the failures and fix the code under `taskflow/` until the whole\nsuite passes. Treat the tests as the specification \u2014 they encode the intended\nbehavior; do not modify them.\n\nA few things worth knowing before you dive in:\n\n- **The failures can be misleading.** A test named for one module may be\n failing because of a defect in a *different* module. Read each failure for\n what it actually asserts, then trace the behavior back to its root cause\n rather than patching the nearest line.\n- **Fixing one bug can uncover another.** Some defects are masked by others:\n correcting the first makes a second one visible (a test that was failing for\n reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose\n more than once.\n- **The defects are genuine logic errors**, not typos that raise on import. The\n code reads as intended-but-wrong: comments and docstrings describe the\n correct behavior, so compare what each function *says* it does against what it\n *actually* does.\n\nDone when `python3 -m unittest` reports every test passing.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "25/25 tests passing\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 3550, "mtime": 1784160991.224065, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784160991.2266393, "sha256": ""}, "tests/test_config.py": {"bytes": 1982, "mtime": 1784160991.226713, "sha256": ""}, "tests/test_events.py": {"bytes": 570, "mtime": 1784160991.2267613, "sha256": ""}, "tests/test_pipelines.py": {"bytes": 12337, "mtime": 1784160991.2268624, "sha256": ""}, 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"devin:webcore:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T08:40:54", "harness": "devin", "model": "gpt-5.6-sol", "task": "webcore", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 538.039, "t_env_setup_s": 0.014, "t_agent_s": 538.039, "t_checker_s": 0.236, "tokens": 103726, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 1664874, "total_completion_tokens": 24355, "total_cached_tokens": 1585503, "total_steps": 41, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 33, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_qdbdm9gg.json", "--", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, 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null, "wall_time_s": 495.237, "t_env_setup_s": 0.012, "t_agent_s": 495.237, "t_checker_s": 0.24, "tokens": 97601, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 1972802, "total_completion_tokens": 22086, "total_cached_tokens": 1897287, "total_steps": 45, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 37, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_ccax156n.json", "--", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, 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"wall_time_s": 515.827, "t_env_setup_s": 0.012, "t_agent_s": 515.827, "t_checker_s": 0.24, "tokens": 91955, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 1975313, "total_completion_tokens": 22787, "total_cached_tokens": 1906145, "total_steps": 49, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 41, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_0pvcqw9a.json", "--", "# Add typed routing, mounting, and content-negotiation to webcore\n\n`webcore` is our small in-process web framework (see `webcore/README.md` for the\nmodule layout and entry points). It already handles the basics and we want to\ngrow its routing layer into something production-shaped. This is a single,\nconnected piece of work: converters, routing precedence, sub-application\nmounting, and HTTP method handling all interact, so please implement the whole\nthing coherently rather than in isolated patches.\n\n## What already works (do not break it)\n\nThe following behaviour exists today and must keep working exactly as it does\nnow:\n\n* **Static routing** \u2014 `@app.route(\"/items\")` matches `GET /items`.\n* **A single string path parameter** \u2014 `@app.route(\"/greet/{name}\")` captures one\n non-empty segment (no `/`) and passes it to the handler as `name=\"...\"`.\n A handler is called as `handler(request, **path_params)`.\n* **GET/POST dispatch** \u2014 a route declares `methods=[...]` (default `[\"GET\"]`);\n the matching route for the request's method is invoked.\n* **Responses** \u2014 `text(body, status=200, headers=None)` returns a\n `text/plain; charset=utf-8` response; `json_response(obj)` returns\n `application/json`. A handler may also return a bare `str` (becomes text) or a\n `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a\n case-insensitive map), `.text`, and `.json()`.\n* **Request query parsing** \u2014 `Request(method, path)` splits any `?query` off the\n path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,\n percent- and `+`-decoded).\n* **The middleware onion (single app)** \u2014 `app.use(mw)` registers a middleware\n `mw(request, next) -> response`. Middlewares run outermost-first on the way in\n and unwind on the way out; the first registered is the outermost. A middleware\n that returns a response without calling `next` short-circuits the inner layers\n and the handler.\n* **404** \u2014 a request whose path matches no route returns status 404.\n* **TestClient** \u2014 `TestClient(app)` with `.get/.post/...` and\n `.request(method, path, follow_redirects=False)`.\n\nKeep all of that intact. The work below extends the routing and dispatch layers\naround it.\n\n## What to build\n\n### 1. Typed path converters\n\nSupport typed placeholders in route patterns. A placeholder always occupies a\nwhole path segment and is written `{name}` or `{name:converter}`:\n\n* `{name}` \u2014 the existing behaviour: exactly one non-empty segment, no `/`,\n delivered as a `str`. This is the `str` converter and stays the default.\n* `{id:int}` \u2014 one or more ASCII digits (`[0-9]+`) **only**. The handler receives\n a Python `int`.\n* `{s:slug}` \u2014 matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined\n by single hyphens). Delivered as a `str`.\n* `{rest:path}` \u2014 one or more segments **including `/`**. Delivered as a `str`\n that may contain `/`. Because it swallows slashes it is only legal as the\n **last** segment of a pattern; a `path` converter anywhere else is an error.\n\n### 2. Converter fall-through\n\nConverter matching is part of route matching. If a segment fails its converter\n(for example `abc` against `{id:int}`), that route simply does **not** match, and\nmatching continues to the remaining routes. If no route matches, the result is a\n404 \u2014 a non-matching converter must never raise or produce a 500.\n\n### 3. Route precedence\n\nWhen more than one route could match a path, resolve the ambiguity by\nspecificity, not by luck of registration order:\n\n* At any given position, a **static** segment beats a **dynamic** one.\n* More-static routes are tried before more-dynamic ones.\n* A `{...:path}` route has the **lowest** precedence of all.\n* A genuine tie (same shape) is broken by registration order (earlier wins).\n\nSo with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`\nhits the static route and `GET /users/7` hits the dynamic one \u2014 regardless of\nwhich was registered first.\n\n### 4. Handler parameter types\n\nThe value a handler receives matches the converter: `int` parameters arrive as\n`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`\nthat may contain `/`.\n\n### 5. Sub-application mounting\n\nAdd `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may\ncontain parameters, e.g. `app.mount(\"/api/{ver}\", subapp)`. A request whose path\nstarts with the prefix is routed into the sub-application against the remainder\nof the path. The handler receives **both** the prefix parameters and the\nsub-route parameters, merged into one set of keyword arguments. For example, with\n`sub` holding `@sub.route(\"/users/{id:int}\")` and `app.mount(\"/api/{ver}\", sub)`,\n`GET /api/v2/users/9` invokes the handler with `ver=\"v2\"` and `id=9`. Mounts may\nnest.\n\n### 6. Middleware ordering across a mount\n\nMiddleware follows the same onion model through a mount. On the way in, the\nparent app's middlewares run first, then the mounted sub-app's middlewares, then\nthe handler; on the way out it unwinds child-then-parent. A middleware that\nreturns a response without calling `next` still short-circuits everything inside\nit \u2014 including a mounted sub-app's middlewares and handler.\n\n### 7. Trailing-slash redirect\n\nA route has one canonical form: whatever it was registered as. A request for the\nother trailing-slash form redirects to the canonical one with a **308** and a\n`Location` header pointing at the canonical path. Any query string is preserved\non the redirect target.\n\n* Registered `/items`, request `GET /items/` \u2192 308, `Location: /items`.\n* Registered `/items`, request `GET /items/?a=1&b=2` \u2192 308,\n `Location: /items?a=1&b=2`.\n* Registered `/dir/`, request `GET /dir` \u2192 308, `Location: /dir/`.\n\n### 8. Method mismatch \u2192 405\n\nIf a path matches a route but none of the routes on that path allow the\nrequest's method, return **405** (not 404) with an `Allow` header whose value is\nthe allowed methods for that path, **sorted and comma-separated** (e.g.\n`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause\n10.\n\n### 9. `url_for(name, **params)`\n\nReverse-generate a URL for a named route:\n\n* Fill the route's parameters from `params`, formatting each through its\n converter (an `int` renders as its decimal string; a `slug`/`str` is validated\n against its pattern).\n* Include the mount prefix, filled with its own parameters, when the named route\n lives inside a mounted sub-app.\n* Any keyword arguments left over after the path is filled become a query string:\n URL-encoded, **sorted by key**, and appended after `?`.\n* A missing or invalid parameter raises a clear error (`KeyError` or\n `ValueError`).\n\nFor example, `url_for(\"user\", id=7, q=\"hello world\", page=2)` for\n`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.\n\n### 10. Automatic HEAD and OPTIONS\n\n* A route registered for `GET` also answers `HEAD`: same status and headers as\n the `GET` response, but an **empty body**.\n* `OPTIONS` on a path that matches at least one route returns **204** with an\n `Allow` header (sorted, comma-separated) listing the methods that path\n supports \u2014 including the auto `HEAD` (when `GET` is present) and `OPTIONS`\n itself.\n\nImplement all ten together so the pieces compose: a mounted, parametric route\nwith a typed parameter should still honour precedence, method handling, and\n`url_for`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "regression 19/19 feature 24/24\nSCORE: 1.0000\n", "checker_stderr": "", "checker_workspace_files": {"README.md": {"bytes": 4651, "mtime": 1784160991.2288942, "sha256": ""}, 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"sha256": ""}, "webcore/templating.py": {"bytes": 20066, "mtime": 1784160991.2307742, "sha256": ""}, "webcore/testclient.py": {"bytes": 2585, "mtime": 1784160991.2308257, "sha256": ""}, "webcore/testutil.py": {"bytes": 7885, "mtime": 1784160991.230871, "sha256": ""}, "webcore/urls.py": {"bytes": 3174, "mtime": 1784160991.2309117, "sha256": ""}, "webcore/validators.py": {"bytes": 7857, "mtime": 1784160991.2309563, "sha256": ""}, "webcore/views.py": {"bytes": 4722, "mtime": 1784160991.2309983, "sha256": ""}, "webcore/wsgi.py": {"bytes": 5153, "mtime": 1784160991.2310467, "sha256": ""}, "tests/__init__.py": {"bytes": 0, "mtime": 1784639176.8581817, "sha256": ""}, "tests/test_routing_features.py": {"bytes": 6909, "mtime": 1784639112.6426446, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T09:06:44", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 185.337, "t_env_setup_s": 0.001, "t_agent_s": 185.337, "t_checker_s": 0.23, "tokens": 30150, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 374810, "total_completion_tokens": 5806, "total_cached_tokens": 350466, "total_steps": 30, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 21, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_n8owa3ns.json", "--", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784639383.8603692, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784639349.6049657, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784639366.7047458, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T09:09:50", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 68.038, "t_env_setup_s": 0.001, "t_agent_s": 68.038, "t_checker_s": 0.171, "tokens": 19486, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 176320, "total_completion_tokens": 3212, "total_cached_tokens": 160046, "total_steps": 20, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 13, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_xcqp8gjy.json", "--", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: main.db-wal is missing; the repaired WAL file must be present\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784639453.3925679, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784639445.58561, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/db-wal-recovery:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T09:10:58", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/db-wal-recovery", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 76.803, "t_env_setup_s": 0.001, "t_agent_s": 76.803, "t_checker_s": 0.222, "tokens": 18883, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 156609, "total_completion_tokens": 3233, "total_cached_tokens": 140959, "total_steps": 19, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 12, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_55ilkzs5.json", "--", "I have a database in WAL (Write-Ahead Logging) mode in the working directory.\nHowever, the WAL file appears to be corrupted or encrypted. When you try to\naccess the database, SQLite may only show the base data (5 records) instead of\nall 11 records that should be there.\n\nYour task is to:\n\n1. Fix the WAL file so SQLite can read it.\n2. Extract **all** data from the database, including WAL changes.\n3. Create a JSON file named `recovered.json` in the working directory.\n\nThe output should have the format:\n\n```json\n[{\"id\": 1, \"name\": \"item1\", \"value\": 123}, {\"id\": 2, \"name\": \"item2\", \"value\": 456}]\n```\n\nThe list must be sorted by `id`, and each object must have integer `id`, string\n`name`, and integer `value` fields. You should recover all 11 records total.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: recovered.json and repaired main.db-wal match all recovered WAL rows\n", "checker_stderr": "", "checker_workspace_files": {"main.db": {"bytes": 8192, "mtime": 1784160991.2070532, "sha256": ""}, "main.db-shm": {"bytes": 32768, "mtime": 1784639530.0971541, "sha256": ""}, "main.db-wal": {"bytes": 16512, "mtime": 1784639508.0143416, "sha256": ""}, "main.db-wal.corrupt": {"bytes": 16512, "mtime": 1784639508.0137491, "sha256": ""}, "recovered.json": {"bytes": 473, "mtime": 1784639522.0319657, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/extract-elf:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T09:12:15", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 125.553, "t_env_setup_s": 0.001, "t_agent_s": 125.553, "t_checker_s": 0.284, "tokens": 24875, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 201546, "total_completion_tokens": 5166, "total_cached_tokens": 181837, "total_steps": 20, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 13, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_rwob6eay.json", "--", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3213, "mtime": 1784639612.5955212, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/extract-elf:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T09:14:21", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 114.407, "t_env_setup_s": 0.001, "t_agent_s": 114.407, "t_checker_s": 0.289, "tokens": 25983, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 229538, "total_completion_tokens": 4421, "total_cached_tokens": 207976, "total_steps": 21, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 14, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_z1v9mlsq.json", "--", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 2421, "mtime": 1784639734.555411, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/extract-elf:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T09:16:16", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/extract-elf", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 93.204, "t_env_setup_s": 0.001, "t_agent_s": 93.204, "t_checker_s": 0.24, "tokens": 22776, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 186895, "total_completion_tokens": 4072, "total_cached_tokens": 168191, "total_steps": 20, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 13, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_f24dbvwk.json", "--", "I have provided a file `a.out` in the working directory. It is a compiled C\nbinary. Write a program `extract.js` that, when run as:\n\n```bash\nnode extract.js a.out > out.json\n```\n\nextracts memory values from the binary and outputs them as a JSON object with\nmemory addresses as keys and their values as integers.\n\nExample output format:\n\n```json\n{\"4194304\": 1784774249, \"4194308\": 1718378344}\n```\n\nSuccess criteria:\n\n1. For any address you include in your output, the value must match the\n reference solution. Addresses with incorrect values fail the test.\n2. You need to extract at least 75% of the memory values that are present in the\n reference solution.\n\nThe output values should be integers, not strings.\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: included addresses outside the checker-owned reference map: ['4194304', '4194308', '4194312', '4194316', '4194320']\n", "checker_stderr": "", "checker_workspace_files": {"a.out": {"bytes": 70472, "mtime": 1784160991.2077057, "sha256": ""}, "extract.js": {"bytes": 3341, "mtime": 1784639844.753728, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T09:17:49", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 1, "success": false, "completed": false, "error": "exit 1", "wall_time_s": 99.948, "t_env_setup_s": 0.001, "t_agent_s": 99.948, "t_checker_s": 0.225, "tokens": 17025, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 86098, "total_completion_tokens": 2883, "total_cached_tokens": 71956, "total_steps": 14, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 7, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_n0yms7gf.json", "--", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": false, "checker_stdout": "FAIL: attack.py does not exist\n", "checker_stderr": "", "checker_workspace_files": {"feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T09:19:29", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 2, "success": true, "completed": false, "error": "exit 1", "wall_time_s": 155.664, "t_env_setup_s": 0.001, "t_agent_s": 155.664, "t_checker_s": 0.468, "tokens": 20473, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 119223, "total_completion_tokens": 4548, "total_cached_tokens": 103298, "total_steps": 16, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 9, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_t_v7ljsp.json", "--", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 1585, "mtime": 1784640084.3339977, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/feal-differential-cryptanalysis:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T09:22:06", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/feal-differential-cryptanalysis", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 95.004, "t_env_setup_s": 0.001, "t_agent_s": 95.004, "t_checker_s": 0.512, "tokens": 19539, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 146734, "total_completion_tokens": 3833, "total_cached_tokens": 131028, "total_steps": 18, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 11, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_xtg2us06.json", "--", "The file `feal.py` (in the working directory) implements a FEAL-like encryption\nfunction. Implement a chosen-plaintext attack that recovers the value of\n`key[5]`.\n\nYour attack must be implemented in a file `attack.py` in the working directory,\nand must define a function `attack(encrypt_fn)` that returns the uint32 value of\n`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as\n`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer\nciphertext, using secret round keys you cannot read directly). Your attack should\nrun in less than 30 seconds.\n\nEach of the 6 round keys is derived from a 16-bit seed. You still can't brute\nforce the entire key space, but this should make it easier to perform a\ndifferential attack that recovers the round key.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: attack recovered key[5] on all 5 trials\n", "checker_stderr": "", "checker_workspace_files": {"attack.py": {"bytes": 1701, "mtime": 1784640193.6918597, "sha256": ""}, "feal.py": {"bytes": 1951, "mtime": 1784160991.208084, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/gcode-to-text:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T09:23:41", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 111.672, "t_env_setup_s": 0.003, "t_agent_s": 111.672, "t_checker_s": 0.245, "tokens": 59649, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 409120, "total_completion_tokens": 4941, "total_cached_tokens": 354412, "total_steps": 25, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 18, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_w8qaqa4v.json", "--", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784640325.0785644, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/gcode-to-text:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T09:25:33", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 208.396, "t_env_setup_s": 0.003, "t_agent_s": 208.396, "t_checker_s": 0.169, "tokens": 68483, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 472189, "total_completion_tokens": 9827, "total_cached_tokens": 413533, "total_steps": 29, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 21, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_nl57dlcl.json", "--", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784640532.7467754, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/gcode-to-text:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T09:29:02", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/gcode-to-text", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 157.715, "t_env_setup_s": 0.002, "t_agent_s": 157.715, "t_checker_s": 0.173, "tokens": 68336, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 465937, "total_completion_tokens": 6999, "total_cached_tokens": 404600, "total_steps": 27, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 19, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_cnsxhh07.json", "--", "I have provided a file called `text.gcode` in the working directory. This is a\nG-code file for a Prusa MK4S. I am trying to print some text onto an existing\nobject.\n\nWhen I run the print, what will the text show? Write the answer to `out.txt` in\nthe working directory.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: out.txt matches checker-owned decoded text\n", "checker_stderr": "", "checker_workspace_files": {"out.txt": {"bytes": 27, "mtime": 1784640688.5803485, "sha256": ""}, "text-crop.pgm": {"bytes": 4480017, "mtime": 1784640664.8304582, "skipped": "too_large>2097152"}, "text-crop.png": {"bytes": 78556, "mtime": 1784640664.8653066, "sha256": ""}, "text-view.pgm": {"bytes": 3600016, "mtime": 1784640641.9826841, "skipped": "too_large>2097152"}, "text-view.png": {"bytes": 127613, "mtime": 1784640642.0186045, "sha256": ""}, "text.gcode": {"bytes": 1661422, "mtime": 1784160991.213857, "sha256": ""}, "toolpaths.png": {"bytes": 57851, "mtime": 1784640616.123616, "sha256": ""}, "toolpaths.ppm": {"bytes": 6480017, "mtime": 1784640616.0690029, "skipped": "too_large>2097152"}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T09:31:39", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 178.471, "t_env_setup_s": 0.003, "t_agent_s": 178.471, "t_checker_s": 0.24, "tokens": 40051, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 374531, "total_completion_tokens": 8831, "total_cached_tokens": 343311, "total_steps": 23, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 16, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_cusemjer.json", "--", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/shape_aware_packer.py": {"bytes": 2064, "mtime": 1784640831.0667164, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784640834.0575821, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 88800, "mtime": 1784640834.0604935, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T09:34:38", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 2, "success": true, "completed": true, "error": null, "wall_time_s": 193.765, "t_env_setup_s": 0.002, "t_agent_s": 193.765, "t_checker_s": 0.227, "tokens": 38506, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 411490, "total_completion_tokens": 9479, "total_cached_tokens": 382463, "total_steps": 25, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 18, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_dnt0tyo2.json", "--", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/optimize_plan.py": {"bytes": 4709, "mtime": 1784641047.8859954, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784641054.5234966, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784641054.5247462, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/llm-inference-batching-scheduler:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T09:37:52", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/llm-inference-batching-scheduler", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 239.752, "t_env_setup_s": 0.002, "t_agent_s": 239.752, "t_checker_s": 0.24, "tokens": 55070, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 664491, "total_completion_tokens": 9944, "total_cached_tokens": 619365, "total_steps": 26, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 19, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_5ibscmxn.json", "--", "In this task you will implement an **LLM inference batching scheduler\n(shape-aware)** for a static-graph LLM inference system.\n\n**Background**\n\nWhen running large language models on hardware accelerators (such as TPUs, RDUs,\nor bespoke inference chips) the compiled execution graph has to operate on\nfixed-sized tensors. Requests arrive with arbitrary prompt lengths and\ngeneration lengths, but at runtime everything must be rounded up to a multiple of\na common granularity. A naive strategy of batching each request independently\nwastes compute, increases the number of cold compilations, and leads to poor\nend-to-end latency.\n\n**Goal**\n\nRead the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`\nand `task_file/input_data/requests_bucket_2.jsonl` and produce plans in\n`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`\n(all paths relative to the working directory) that assign every request to a\nbatch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,\n`shape.hidden_align`). Each input entry is a JSON object with:\n\n * `request_id`: a unique string identifier (e.g. `\"r-000010\"`)\n * `prompt_len`: the input prompt length in tokens (integer)\n * `gen_len`: the number of tokens to generate (integer)\n\nPack these into batches so that:\n\n * All input requests are included exactly once (no missing/duplicate request_ids).\n * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where\n seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.\n * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both\n buckets (MAX_SHAPES=8).\n * One record per request_id, identical shapes within each batch_id.\n\nTo aid development you are given (in the working directory):\n\n * `task_file/scripts/cost_model.py` \u2013 an analytical cost and latency model.\n * `task_file/scripts/baseline_packer.py` \u2013 a slow baseline to compare against.\n\n**Cost model**\n\n`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform\nyour packing strategy. During evaluation, a copy of this cost model is used to\nmeasure your solution's performance.\n\n * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).\n * Decode cost/latency depend on the batch decode bound `G_max` and `S`.\n * There is a per-batch overhead cost/latency term.\n * There is a per-shape compilation/bring-up cost over the set of unique shapes.\n * `pad_ratio` is padded tokens divided by real tokens.\n\n**Thresholds** (your plans must be strictly below all of these):\n\n| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |\n|-------|------|-----------|------------------|--------------------------|\n| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |\n| requests_bucket_2.jsonl | 4.8e10 | 0.15 | 2.1e5 | 3.2e7 |\n\n**Deliverables**\n\nGenerate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and\n`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the\nconstraints and thresholds above, and you must keep the `input_data` files\nunchanged. Example output line:\n\n```\n{\"request_id\": \"r-000010\", \"batch_id\": \"b-0002\", \"shape\": {\"seq_align\": 192, \"heads_align\": 32, \"hidden_align\": 4096}}\n```\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: plans satisfy all schema, coverage, and performance checks\n", "checker_stderr": "", "checker_workspace_files": {"task_file/scripts/__init__.py": {"bytes": 0, "mtime": 1784160991.214995, "sha256": ""}, "task_file/scripts/baseline_packer.py": {"bytes": 4380, "mtime": 1784160991.2150757, "sha256": ""}, "task_file/scripts/cost_model.py": {"bytes": 8096, "mtime": 1784160991.21517, "sha256": ""}, "task_file/scripts/shape_aware_packer.py": {"bytes": 4265, "mtime": 1784641268.6669757, "sha256": ""}, "task_file/output_data/plan_b1.jsonl": {"bytes": 88000, "mtime": 1784641271.9005032, "sha256": ""}, "task_file/output_data/plan_b2.jsonl": {"bytes": 89600, "mtime": 1784641272.6338983, "sha256": ""}, "task_file/input_data/requests_bucket_1.jsonl": {"bytes": 48964, "mtime": 1784160991.2147944, "sha256": ""}, "task_file/input_data/requests_bucket_2.jsonl": {"bytes": 50219, "mtime": 1784160991.2149184, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/raman-fitting:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T09:41:52", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 1, "success": false, "completed": true, "error": null, "wall_time_s": 148.764, "t_env_setup_s": 0.001, "t_agent_s": 148.764, "t_checker_s": 0.176, "tokens": 30148, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 254567, "total_completion_tokens": 7086, "total_cached_tokens": 231505, "total_steps": 22, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 15, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_8poa142v.json", "--", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 231, "mtime": 1784641445.5462778, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/raman-fitting:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T09:44:21", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 162.7, "t_env_setup_s": 0.001, "t_agent_s": 162.7, "t_checker_s": 0.226, "tokens": 30674, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 293964, "total_completion_tokens": 7173, "total_cached_tokens": 270463, "total_steps": 24, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 17, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_iq9a_r7b.json", "--", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: 2D parameters outside tolerance: offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 237, "mtime": 1784641616.4460647, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/raman-fitting:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T09:47:04", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/raman-fitting", "trial": 3, "success": false, "completed": true, "error": null, "wall_time_s": 187.119, "t_env_setup_s": 0.001, "t_agent_s": 187.119, "t_checker_s": 0.223, "tokens": 33616, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 310944, "total_completion_tokens": 7357, "total_cached_tokens": 284685, "total_steps": 24, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 17, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_2oj9dwg9.json", "--", "You are given the output file of a Raman setup, `graphene.dat`, in the working\ndirectory. It was used to measure a graphene sample.\n\nFit the G and 2D peaks of the spectrum and write the fitted peak parameters to\n`results.json` in the working directory. The required parameters are `x0`,\n`gamma`, `amplitude`, and `offset` for each peak.\n\nThe file should have the following format:\n\n```json\n{\n \"G\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n },\n \"2D\": {\n \"x0\": ,\n \"gamma\": ,\n \"amplitude\": ,\n \"offset\": \n }\n}\n```\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: G parameters outside tolerance: x0, gamma, amplitude, offset\n", "checker_stderr": "", "checker_workspace_files": {"graphene.dat": {"bytes": 88805, "mtime": 1784160991.2160459, "sha256": ""}, "results.json": {"bytes": 247, "mtime": 1784641802.7980077, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial1", "ts_iso": "2026-07-21T09:50:11", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 1, "success": true, "completed": true, "error": null, "wall_time_s": 1758.458, "t_env_setup_s": 0.018, "t_agent_s": 1758.458, "t_checker_s": 33.968, "tokens": 101248, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 4458975, "total_completion_tokens": 29511, "total_cached_tokens": 4387238, "total_steps": 94, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 87, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_vz0x86g5.json", "--", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"callback-test.txt": {"bytes": 23, "mtime": 1784643494.1752105, "sha256": ""}, "eval.scm": {"bytes": 8084, "mtime": 1784643540.5742135, "sha256": ""}, "handler-test.txt": {"bytes": 23, "mtime": 1784643501.2778285, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, "sha256": ""}, "test/06-interactive-io.scm": {"bytes": 992, "mtime": 1784160991.2214541, "sha256": ""}, "test/08-progn-sequencing.scm": {"bytes": 1067, "mtime": 1784160991.2214954, "sha256": ""}, "test/09-mutual-recursion.scm": {"bytes": 895, "mtime": 1784160991.2215376, "sha256": ""}, "test/10-advanced-features.scm": {"bytes": 1945, "mtime": 1784160991.2215784, "sha256": ""}, "test/README.md": {"bytes": 2108, "mtime": 1784160991.2216206, "sha256": ""}, "test/accumulator_patterns.scm": {"bytes": 1410, "mtime": 1784160991.2216601, "sha256": ""}, "test/binary_tree.scm": {"bytes": 1311, "mtime": 1784160991.2217, "sha256": ""}, "test/calculator.scm": {"bytes": 341, "mtime": 1784160991.2217395, "sha256": ""}, "test/church_numerals.scm": {"bytes": 1179, "mtime": 1784160991.2217798, "sha256": 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1133, "mtime": 1784160991.2222624, "sha256": ""}, "test/oeis_sequences.scm": {"bytes": 2009, "mtime": 1784160991.2223027, "sha256": ""}, "test/oeis_sequences2.scm": {"bytes": 2351, "mtime": 1784160991.222343, "sha256": ""}, "test/oeis_sequences3.scm": {"bytes": 2555, "mtime": 1784160991.2223866, "sha256": ""}, "test/recursive_structures.scm": {"bytes": 2780, "mtime": 1784160991.2224295, "sha256": ""}, "test/test_read.scm": {"bytes": 84, "mtime": 1784160991.222468, "sha256": ""}, "test/variadic_functions.scm": {"bytes": 2468, "mtime": 1784160991.2225044, "sha256": ""}, "test/y_combinator.scm": {"bytes": 846, "mtime": 1784160991.2225444, "sha256": ""}, "shadow_test/01-factorial.scm": {"bytes": 548, "mtime": 1784160991.2199306, "sha256": ""}, "shadow_test/02-fibonacci.scm": {"bytes": 811, "mtime": 1784160991.2199712, "sha256": ""}, "shadow_test/03-list-operations.scm": {"bytes": 1198, "mtime": 1784160991.2200122, "sha256": ""}, "shadow_test/04-higher-order.scm": {"bytes": 988, "mtime": 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"shadow_test/currying.scm": {"bytes": 1610, "mtime": 1784160991.2204933, "sha256": ""}, "shadow_test/filter_operations.scm": {"bytes": 749, "mtime": 1784160991.2205327, "sha256": ""}, "shadow_test/fold_operations.scm": {"bytes": 821, "mtime": 1784160991.2206206, "sha256": ""}, "shadow_test/function_composition.scm": {"bytes": 1194, "mtime": 1784160991.2206597, "sha256": ""}, "shadow_test/lazy_evaluation.scm": {"bytes": 1294, "mtime": 1784160991.2206986, "sha256": ""}, "shadow_test/list_operations.scm": {"bytes": 1680, "mtime": 1784160991.220741, "sha256": ""}, "shadow_test/map_operations.scm": {"bytes": 651, "mtime": 1784160991.2207797, "sha256": ""}, "shadow_test/memoization.scm": {"bytes": 1644, "mtime": 1784160991.2208223, "sha256": ""}, "shadow_test/mutual_recursion.scm": {"bytes": 1913, "mtime": 1784160991.220859, "sha256": ""}, "shadow_test/nested_defines.scm": {"bytes": 1154, "mtime": 1784160991.2209008, "sha256": ""}, "shadow_test/oeis_sequences.scm": {"bytes": 1943, "mtime": 1784160991.2209404, "sha256": ""}, "shadow_test/oeis_sequences2.scm": {"bytes": 2364, "mtime": 1784160991.2209766, "sha256": ""}, "shadow_test/oeis_sequences3.scm": {"bytes": 2565, "mtime": 1784160991.2210133, "sha256": ""}, "shadow_test/recursive_structures.scm": {"bytes": 2994, "mtime": 1784160991.221053, "sha256": ""}, "shadow_test/test_read.scm": {"bytes": 90, "mtime": 1784160991.22109, "sha256": ""}, "shadow_test/variadic_functions.scm": {"bytes": 2434, "mtime": 1784160991.221138, "sha256": ""}, "shadow_test/y_combinator.scm": {"bytes": 877, "mtime": 1784160991.2211754, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial2", "ts_iso": "2026-07-21T10:20:04", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 2, "success": false, "completed": true, "error": null, "wall_time_s": 2084.471, "t_env_setup_s": 0.013, "t_agent_s": 2084.471, "t_checker_s": 1.427, "tokens": 130193, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 6017572, "total_completion_tokens": 42699, "total_cached_tokens": 5930078, "total_steps": 107, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 100, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_uappc_io.json", "--", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"], "checker_exit": 1, "exec_mode": "local", "score": 0.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "wrong_answer", "failure_reason": null, "workspace_changed": true, "checker_stdout": "FAIL: /tasks-imported/terminal-bench/schemelike-metacircular-eval/checker_data/shadow_test/05-simple-io.scm: self-hosted eval.scm output != interp.py output\n direct: 'Starting I/O tests...\\nText: Greetings, Scheme!\\nInteger: 99\\nTrue value: True\\nFalse value: False\\nPair list: (10 20 30 40)\\nLetters: X Y Z\\nSequential outputs: Alpha Beta Gamma\\nConditional test: 2 is less than 7\\nI/O testing finished!'\n via meta: \"[RECURSION] Deep recursion detected! Last expression: 'base-car'\\nUnexpected error: Recursion limit exceeded\"\n", "checker_stderr": "", "checker_workspace_files": {"eval.scm": {"bytes": 13508, "mtime": 1784645632.9216704, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, "sha256": ""}, "test/06-interactive-io.scm": {"bytes": 992, "mtime": 1784160991.2214541, "sha256": ""}, "test/08-progn-sequencing.scm": {"bytes": 1067, "mtime": 1784160991.2214954, "sha256": ""}, "test/09-mutual-recursion.scm": {"bytes": 895, "mtime": 1784160991.2215376, "sha256": ""}, 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"shadow_test/list_operations.scm": {"bytes": 1680, "mtime": 1784160991.220741, "sha256": ""}, "shadow_test/map_operations.scm": {"bytes": 651, "mtime": 1784160991.2207797, "sha256": ""}, "shadow_test/memoization.scm": {"bytes": 1644, "mtime": 1784160991.2208223, "sha256": ""}, "shadow_test/mutual_recursion.scm": {"bytes": 1913, "mtime": 1784160991.220859, "sha256": ""}, "shadow_test/nested_defines.scm": {"bytes": 1154, "mtime": 1784160991.2209008, "sha256": ""}, "shadow_test/oeis_sequences.scm": {"bytes": 1943, "mtime": 1784160991.2209404, "sha256": ""}, "shadow_test/oeis_sequences2.scm": {"bytes": 2364, "mtime": 1784160991.2209766, "sha256": ""}, "shadow_test/oeis_sequences3.scm": {"bytes": 2565, "mtime": 1784160991.2210133, "sha256": ""}, "shadow_test/recursive_structures.scm": {"bytes": 2994, "mtime": 1784160991.221053, "sha256": ""}, "shadow_test/test_read.scm": {"bytes": 90, "mtime": 1784160991.22109, "sha256": ""}, "shadow_test/variadic_functions.scm": {"bytes": 2434, "mtime": 1784160991.221138, "sha256": ""}, "shadow_test/y_combinator.scm": {"bytes": 877, "mtime": 1784160991.2211754, "sha256": ""}}, "image_digest": null, "candidate_provenance": null, "version_drift": false, "timeout_s": 2400, "workspace_source": null} {"run_id": "devin:terminal-bench/schemelike-metacircular-eval:gpt-5.6-sol:trial3", "ts_iso": "2026-07-21T10:54:50", "harness": "devin", "model": "gpt-5.6-sol", "task": "terminal-bench/schemelike-metacircular-eval", "trial": 3, "success": true, "completed": true, "error": null, "wall_time_s": 1652.74, "t_env_setup_s": 0.014, "t_agent_s": 1652.74, "t_checker_s": 32.625, "tokens": 85643, "tokens_input_uncached": null, "tokens_cache_read": null, "tokens_cache_write": null, "tokens_output": null, "tokens_reasoning": null, "usage_raw": {"total_prompt_tokens": 2779478, "total_completion_tokens": 25177, "total_cached_tokens": 2719012, "total_steps": 68, "serving_path": "devin-cloud"}, "token_basis": "estimated", "tokens_proxy_input_uncached": null, "tokens_proxy_cache_read": null, "tokens_proxy_cache_write": null, "tokens_proxy_output": null, "tokens_proxy_reasoning": null, "tokens_proxy_calls": null, "sampling_observed": null, "token_basis_proxy": null, "proxy_capture_truncated": null, "tokens_fresh": null, "turns": 61, "cmd": ["devin", "-p", "--permission-mode", "dangerous", "--model", "gpt-5-6-sol-medium", "--export", "/devin_export_nrvl208a.json", "--", "Write a file `eval.scm` that is a metacircular evaluator capable of interpreting\nthe scheme-like language implemented by `interp.py` (in the working directory).\n\nThe `eval.scm` file should read exactly one line from STDIN: this will be a file\npath to a new `.scm` file that should be interpreted. Any remaining input should\nbe redirected to the interpreted program, and any output from that program\nshould be sent to STDOUT.\n\nYour interpreter should be able to interpret (1) each of the test programs in\n`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.\n\nFor example, the following three commands should all do the same thing:\n\n```\necho '(+ 7 8)' | python3 interp.py test/calculator.scm\necho -e 'test/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\necho -e 'eval.scm\\ntest/calculator.scm\\n(+ 7 8)' | python3 interp.py eval.scm\n```\n\n- The first runs `test/calculator.scm` directly under `interp.py`, which reads\n `(+ 7 8)` and adds 7 and 8.\n- The second runs `eval.scm` under `interp.py`; `eval.scm` takes\n `test/calculator.scm` as the file to run and runs it, which then computes 7+8.\n- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;\n the outer `eval.scm` interprets the inner `eval.scm`, which then runs the\n calculator as above.\n\nFor every test program, running it through `eval.scm` (and, for the\nself-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the\nsame output as running it directly through `interp.py`.\n"], "checker_exit": 0, "exec_mode": "local", "score": 1.0, "harness_version": "devin 3000.2.17 (2c489dfc) (/.local/bin/devin)", "harness_version_source": "host", "failure_class": "solved", "failure_reason": null, "workspace_changed": true, "checker_stdout": "PASS: all 63 scheme programs match via eval.scm\n", "checker_stderr": "", "checker_workspace_files": {"eval.scm": {"bytes": 8001, "mtime": 1784647226.248754, "sha256": ""}, "interp.py": {"bytes": 17578, "mtime": 1784160991.2198575, "sha256": ""}, "test/01-factorial.scm": {"bytes": 497, "mtime": 1784160991.2212462, "sha256": ""}, "test/02-fibonacci.scm": {"bytes": 686, "mtime": 1784160991.2212894, "sha256": ""}, "test/03-list-operations.scm": {"bytes": 1033, "mtime": 1784160991.2213295, "sha256": ""}, "test/04-higher-order.scm": {"bytes": 894, "mtime": 1784160991.2213688, "sha256": ""}, "test/05-simple-io.scm": {"bytes": 921, "mtime": 1784160991.2214127, "sha256": ""}, "test/06-interactive-io.scm": {"bytes": 992, "mtime": 1784160991.2214541, "sha256": ""}, "test/08-progn-sequencing.scm": {"bytes": 1067, "mtime": 1784160991.2214954, "sha256": ""}, "test/09-mutual-recursion.scm": {"bytes": 895, "mtime": 1784160991.2215376, "sha256": ""}, "test/10-advanced-features.scm": {"bytes": 1945, "mtime": 1784160991.2215784, "sha256": ""}, "test/README.md": {"bytes": 2108, "mtime": 1784160991.2216206, "sha256": ""}, "test/accumulator_patterns.scm": {"bytes": 1410, "mtime": 1784160991.2216601, "sha256": ""}, "test/binary_tree.scm": {"bytes": 1311, "mtime": 1784160991.2217, "sha256": ""}, "test/calculator.scm": {"bytes": 341, "mtime": 1784160991.2217395, "sha256": ""}, "test/church_numerals.scm": {"bytes": 1179, "mtime": 1784160991.2217798, "sha256": ""}, "test/closures.scm": {"bytes": 904, "mtime": 1784160991.221818, "sha256": ""}, "test/continuation_passing.scm": {"bytes": 1104, "mtime": 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