arXiv cs.AI· Hao Chen, Yukun Yan, Ziyu Han, Ye He, Chuanzhe Guo, Shangzhan Li, Qi Shi, Qingfu Zhu, Maosong Sun, Wanxiang Che·· 3 小时前AI 评分40
RuHarness：评估标准与执行框架协同演化的智能体持续自我改进方法
When the Critic Falls Behind: Co-Evolving Rubrics and Harnesses for Continual Agent Self-Improvement
AI 导读
一篇 arXiv 论文提出 RuHarness，一个 rubric 与 harness 协同演化框架，用于智能体的持续自我改进。该方法从执行轨迹中持续发现并重新评估行为标准，优先处理最相关的未解决弱点，以指导针对性的 harness 更新。
正文 · 原文
Abstract:Autonomous agents increasingly depend on harnesses that govern interaction, memory, tools, and execution around large language models (LLMs). While recent work has begun to evolve these harnesses from execution experience, the supervision used to guide evolution often remains relatively static, making it difficult to track changing behavioral weaknesses across rounds and potentially limiting generalization to unseen tasks. To address this issue, we propose RuHarness, a rubric--harness co-evolution framework for continual agent self-improvement. RuHarness continually discovers and reassesses behavioral criteria from execution trajectories, prioritizes the most relevant unresolved weaknesses, and uses them to guide targeted harness updates while keeping the task objective fixed. Extensive experiments across diverse agent tasks and backbone models show that RuHarness outperforms strong harness-evolution baselines, particularly on unseen held-out tasks. Further analyses show that adaptive rubric prioritization tracks shifting weaknesses and supports more sustained improvement and stable generalization across evolution rounds.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36826 [cs.AI] |
| (or arXiv:2610.36826v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36826 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hao Chen [view email]
[v1]
Tue, 27 Oct 2026 08:24:44 UTC (7,265 KB)
来源：arXiv cs.AI · arxiv.org