ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918508932104192 |
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| author | Lai, Yuxiang Xia, Peng Ji, Haonian Xiong, Kaiwen Zeng, Kaide Liu, Jiaqi Wu, Fang Zhong, Jike Zheng, Zeyu Xie, Cihang Yao, Huaxiu |
| author_facet | Lai, Yuxiang Xia, Peng Ji, Haonian Xiong, Kaiwen Zeng, Kaide Liu, Jiaqi Wu, Fang Zhong, Jike Zheng, Zeyu Xie, Cihang Yao, Huaxiu |
| contents | Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend and revise, while static prompt evaluation misses failures that only appear when agents operate over persistent state. Existing interactive benchmarks have advanced agent evaluation significantly, but most initialize tasks from clean state and do not systematically test how agents handle pre-existing partial, stale, or conflicting artifacts. We present \textbf{ClawForge}, a generator-backed benchmark framework for executable command-line workflows under state conflict. The framework compiles scenario templates, grounded slots, initialized state, reference trajectories, and validators into reproducible task specifications, and evaluates agents step by step over persistent workflow surfaces using normalized end state and observable side effects rather than exact trajectory matching. We instantiate this framework as the ClawForge-Bench (17 scenarios, 6 ability categories). Results across seven frontier models show that the best model reaches only 45.3% strict accuracy, wrong-state replacement remains below 17\% for all models, and the widest model separation (17% to 90%) is driven by whether agents inspect existing state before acting. Partial-credit and step-efficiency analyses further reveal that many failures are near-miss closures rather than early breakdowns, and that models exhibit qualitatively different failure styles under state conflict. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14133 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents Lai, Yuxiang Xia, Peng Ji, Haonian Xiong, Kaiwen Zeng, Kaide Liu, Jiaqi Wu, Fang Zhong, Jike Zheng, Zeyu Xie, Cihang Yao, Huaxiu Artificial Intelligence Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend and revise, while static prompt evaluation misses failures that only appear when agents operate over persistent state. Existing interactive benchmarks have advanced agent evaluation significantly, but most initialize tasks from clean state and do not systematically test how agents handle pre-existing partial, stale, or conflicting artifacts. We present \textbf{ClawForge}, a generator-backed benchmark framework for executable command-line workflows under state conflict. The framework compiles scenario templates, grounded slots, initialized state, reference trajectories, and validators into reproducible task specifications, and evaluates agents step by step over persistent workflow surfaces using normalized end state and observable side effects rather than exact trajectory matching. We instantiate this framework as the ClawForge-Bench (17 scenarios, 6 ability categories). Results across seven frontier models show that the best model reaches only 45.3% strict accuracy, wrong-state replacement remains below 17\% for all models, and the widest model separation (17% to 90%) is driven by whether agents inspect existing state before acting. Partial-credit and step-efficiency analyses further reveal that many failures are near-miss closures rather than early breakdowns, and that models exhibit qualitatively different failure styles under state conflict. |
| title | ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.14133 |