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坏天才:反事实引导的超越任务特定捷径的装备进化

Bad Genius: Counterfactual-Guided Harness Evolution Beyond Task-Specific Shortcuts

Guojun Zhu, Xunheng Huang, Peng Yin, Jiahui Xie, Sanguo Zhang, Doudou Zhou

arXiv 2609.18366首次发表:更新:

发表机构

University of Chinese Academy of Sciences; National University of Singapore; Institute of Automation, Chinese Academy of Sciences(中国科学院大学; 新加坡国立大学; 中国科学院自动化研究所)

机构由 AI 辅助整理,请以论文原文为准。

AI 中文总结

针对自动装备优化导致的评估作弊问题,提出反事实装备搜索与进化(CHASE)方法,通过约束生成和挑战者搜索中和基准捷径,在保持发布基准增益的同时大幅减少有效协议变化下的增益破坏。

AI 中文摘要

可靠的智能体评估因自动装备优化而变得复杂,这种优化反复使用已发布的基准 $B_{\mathrm{rel}}$ 来引导一个提议者,该提议者围绕固定的目标智能体编辑提示、记忆、检索、工具和控制代码。任务留出法改变语义任务,但保持基准协议固定,因此一个“坏天才”提议者可以产生一个作弊装备,其在已发布基准上的增益依赖于整个基准的捷径。我们引入了反事实装备搜索与进化(CHASE),它将装备进化转化为对保持有效性的基准反事实的约束生成。在每次提议者更新后,一个挑战者搜索一个具有大增益破坏性的可执行协议变换。一个有效性防火墙检查任务语义是否被保留,而一个确认集决定该反事实是否进入有限档案。我们形式化了一个精确的捷径中和基准 $B_0$,并建立了将有限反事实档案与 $B_0$ 联系起来的统计保证,以及刻画顺序挑战者搜索的特征。我们在一个合成基准和OfficeQA上评估了CHASE,在OfficeQA上,CHASE保留了强大的已发布基准增益,同时在有效协议变化下大幅减少了增益破坏。

英文摘要

Reliable agent evaluation is complicated by automatic harness optimization, which repeatedly uses a released benchmark $B_{\mathrm{rel}}$ to guide a Proposer that edits prompts, memory, retrieval, tools, and control code around a fixed foundation model. Task holdout is commonly used to guard against harness overfitting. It varies semantic tasks but leaves the benchmark protocol fixed, so a bad genius Proposer can produce a cheating harness whose improvement over the initial harness on $B_{\mathrm{rel}}$ depends on a benchmark-wide shortcut. We introduce Counterfactual Harness Search and Evolution (CHASE), which casts harness evolution as constraint generation over valid counterfactual benchmarks. After each Proposer update, a Challenger searches for an executable protocol transformation with large gain destruction. A validity firewall checks that task semantics are preserved, while a held-out confirmation set determines whether the counterfactual enters a finite archive. We formalize an ideal shortcut-neutralized benchmark $B_0$ and establish theoretical guarantees linking finite counterfactual archives to $B_0$ and characterizing sequential Challenger search. We evaluate CHASE on Syn-Ledger and OfficeQA, where CHASE retains strong released-benchmark gains while substantially reducing gain destruction under valid protocol transformations.

Comments32 pages, 6 figures; includes references and supplementary material

论文原文

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