In this work, we revisit how automatic harness evolution should be evaluated.</p>\n<p>Existing automatic harness evolution methods often search over harnesses using feedback from benchmark tasks and then report final performance on the same benchmark. This makes it difficult to tell whether the gains come from genuinely better and reusable harness design, or simply from spending more inference compute, receiving repeated task feedback, and adapting to the evaluation set.</p>\n<p>We compare harness evolution against simple test time scaling baselines under matched feedback and inference budgets. We also separately test whether evolved harnesses transfer to unseen tasks.</p>\n<p>On Terminal Bench 2.1, harness evolution does not consistently outperform parallel sampling or sequential refinement, either with or without unit test feedback. When the search and evaluation tasks are separated, the evolved harness provides only marginal improvements on held out tasks.</p>\n<p>Our takeaway is not that harness evolution is ineffective or unimportant. Rather, its benefits need to be assessed using fair experimental setups, strong baselines with comparable budgets, and benchmarks that are genuinely sensitive to harness design.</p>\n<p>We hope this work encourages more careful evaluation and helps identify settings where automatic harness evolution can produce real and transferable improvements.</p>\n","updatedAt":"2026-07-18T00:39:06.448Z","author":{"_id":"6536c88d568d8be8fae85541","avatarUrl":"/avatars/8aab923210dad6c09e3649c251b2a391.svg","fullname":"TengXiao","name":"TTTXXX01","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":2,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8824371099472046},"editors":["TTTXXX01"],"editorAvatarUrls":["/avatars/8aab923210dad6c09e3649c251b2a391.svg"],"reactions":[],"isReport":false}},{"id":"6a5af9a79186cfaf6aef7aaf","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":376,"isUserFollowing":false},"createdAt":"2026-07-18T03:57:27.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0](https://huggingface.co/papers/2607.14004) (2026)\n* [SEAGym: An Evaluation Environment for Self-Evolving LLM Agents](https://huggingface.co/papers/2606.17546) (2026)\n* [MemoHarness: Agent Harnesses That Learn from Experience](https://huggingface.co/papers/2607.14159) (2026)\n* [Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents](https://huggingface.co/papers/2605.30621) (2026)\n* [Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference](https://huggingface.co/papers/2606.05922) (2026)\n* [Self-Harness: Harnesses That Improve Themselves](https://huggingface.co/papers/2606.09498) (2026)\n* [TTHE: Test-Time Harness Evolution](https://huggingface.co/papers/2607.08124) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2607.14004\">Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2606.17546\">SEAGym: An Evaluation Environment for Self-Evolving LLM Agents</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.14159\">MemoHarness: Agent Harnesses That Learn from Experience</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.30621\">Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2606.05922\">Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2606.09498\">Self-Harness: Harnesses That Improve Themselves</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.08124\">TTHE: Test-Time Harness Evolution</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code>@librarian-bot recommend</code></p>\n","updatedAt":"2026-07-18T03:57:27.006Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":376,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7400587201118469},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2607.12227","authors":[{"_id":"6a59c69c6c2e371e6ca3830a","name":"Yike Wang","hidden":false},{"_id":"6a59c69c6c2e371e6ca3830b","name":"Huaisheng Zhu","hidden":false},{"_id":"6a59c69c6c2e371e6ca3830c","name":"Zhengyu Hu","hidden":false},{"_id":"6a59c69c6c2e371e6ca3830d","name":"Yige Yuan","hidden":false},{"_id":"6a59c69c6c2e371e6ca3830e","name":"Zhengyu Chen","hidden":false},{"_id":"6a59c69c6c2e371e6ca3830f","name":"Shakti Senthil","hidden":false},{"_id":"6a59c69c6c2e371e6ca38310","name":"Hannaneh Hajishirzi","hidden":false},{"_id":"6a59c69c6c2e371e6ca38311","name":"Yulia Tsvetkov","hidden":false},{"_id":"6a59c69c6c2e371e6ca38312","name":"Pradeep Dasigi","hidden":false},{"_id":"6a59c69c6c2e371e6ca38313","name":"Teng Xiao","hidden":false}],"publishedAt":"2026-07-14T00:00:00.000Z","submittedOnDailyAt":"2026-07-17T00:00:00.000Z","title":"Rethinking the Evaluation of Harness Evolution for Agents","submittedOnDailyBy":{"_id":"6536c88d568d8be8fae85541","avatarUrl":"/avatars/8aab923210dad6c09e3649c251b2a391.svg","isPro":false,"fullname":"TengXiao","user":"TTTXXX01","type":"user","name":"TTTXXX01"},"summary":"We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. First, harness evolution is itself an iterative search procedure that repeatedly evaluates and revises candidate harnesses using task feedback. As in agentic test-time scaling, it should therefore be compared with simple task-level search baselines under matched feedback and inference budgets to determine whether its gains arise from improved harness design or from additional search alone. Second, because the search and the final evaluation share the same benchmark, the reported gains risk overfitting to that specific task set. 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Rethinking the Evaluation of Harness Evolution for Agents
Abstract
We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. First, harness evolution is itself an iterative search procedure that repeatedly evaluates and revises candidate harnesses using task feedback. As in agentic test-time scaling, it should therefore be compared with simple task-level search baselines under matched feedback and inference budgets to determine whether its gains arise from improved harness design or from additional search alone. Second, because the search and the final evaluation share the same benchmark, the reported gains risk overfitting to that specific task set. To address these concerns, we conduct an extensive evaluation comparing harness evolution with simple test-time scaling and discovery baselines under comparable feedback and inference budgets, and also evaluate evolved harnesses on held-out tasks to assess whether the discovered improvements generalize. Experiments on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6 show that automatic harness evolution does not consistently outperform simple test-time scaling methods and exhibits limited generalization. Our results raise important questions about the effectiveness of automatic harness evolution and highlight the need for fairer evaluation protocols and benchmarks for automatic harness design. Our code is available at https://github.com/rethinking-harness-evolution.
Community
In this work, we revisit how automatic harness evolution should be evaluated.
Existing automatic harness evolution methods often search over harnesses using feedback from benchmark tasks and then report final performance on the same benchmark. This makes it difficult to tell whether the gains come from genuinely better and reusable harness design, or simply from spending more inference compute, receiving repeated task feedback, and adapting to the evaluation set.
We compare harness evolution against simple test time scaling baselines under matched feedback and inference budgets. We also separately test whether evolved harnesses transfer to unseen tasks.
On Terminal Bench 2.1, harness evolution does not consistently outperform parallel sampling or sequential refinement, either with or without unit test feedback. When the search and evaluation tasks are separated, the evolved harness provides only marginal improvements on held out tasks.
Our takeaway is not that harness evolution is ineffective or unimportant. Rather, its benefits need to be assessed using fair experimental setups, strong baselines with comparable budgets, and benchmarks that are genuinely sensitive to harness design.
We hope this work encourages more careful evaluation and helps identify settings where automatic harness evolution can produce real and transferable improvements.
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