RuHarness：用动态评估准则驱动智能体自我进化
When the Critic Falls Behind: Co-Evolving Rubrics and Harnesses for Continual Agent Self-Improvement
教智能体自我改进的论文，核心思路是评估准则不能写死，要跟着执行轨迹一起进化，held-out 任务表现更好。
arXiv 论文提出 RuHarness，一个 rubric 与 harness 协同进化框架，用于智能体的持续自我改进。传统 harness 进化依赖固定的监督标准，难以追踪多轮迭代中不断变化的行为弱点。RuHarness 从执行轨迹中持续发现并重新评估行为准则，优先处理最相关的未解决弱点，再指导针对性的 harness 更新。实验覆盖多种智能体任务和骨干模型，RuHarness 在未见过的 held-out 任务上超越强基线，并能在多轮进化中保持更稳定的泛化。
When the Critic Falls Behind: Co-Evolving Rubrics and Harnesses for Continual Agent Self-Improvement
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.
