arXiv AI October 1, 2026 By Meijia Chen, Hao Li, Zheng Lu, Hongshan Lin, Junbai Tian, Yichen Liu, Zijun Tian, Yufan Zou, Shuhan Sun, Hanxin Chen, Zeyu Zhang, Weizhi Du, Yueting Li, Tianyu Shi, Alaa Khamis

False Frontiers: Diagnosing and Mitigating Co-Cheating in Self-Evolving Search Agents

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The paper investigates a failure mode called co‑cheating in self‑evolving search agents, where the proposer and solver agree on shared errors, inflating internal reward without improving external correctness. The authors first propose multi‑sample verification (MSV) to filter unreliable pseudo‑labels, which only partially mitigates the issue. They then introduce CrossFit, a cross‑fitted reward scheme that partitions source documents and uses an auxiliary solver to prevent same‑source agreement, significantly reducing false agreement and boosting downstream benchmark performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 2

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arXiv AI

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llms agents

arXiv AI

Jun 30

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By Aojie Yuan, Yi Nian, Haiyue Zhang, Zijian Su, Yue Zhao

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arXiv AI

Jun 9

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By Zayx Shawn

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arXiv AI

1d ago

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By Xincheng Wei, Yifan Ding, Fucheng Xiong, Yoshua Li, Dongsheng Ma, Rongxiang Weng, Xunliang Cai, Wenjian Ding, Yao Zhang

llms reinforcement-learning benchmarks

arXiv AI

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By Chen Henry Wu, Aditi Raghunathan

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Related stories

DiagEvo: Diagnosis-Guided Self-Evolution via Hierarchical Error Memory

SkillHEX: Improving Agent Skills via Hypothesis-Driven Autonomous Exploration and Exploitation

SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution

PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents

Learning What to Practice: Diagnosis-Guided Self-Evolution for Language Models

Self-Trained Verification for Training- and Test-Time Self-Improvement