让大模型和运行框架一起进化，提升自动化研究效率。
Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents
- 框架与模型交替优化，动态改进运行环境。
- 通过失败轨迹分析，自动修复运行缺陷并提升效率。
- 适合追求自主智能体的科研人员和系统开发者。
自动化人工智能研究的后训练代理需同时优化模型参数与运行时框架（harness），后者决定了研究路径的生成、评估与学习方式。现有方法在固定框架下训练模型，使数据生成过程脱离优化目标，导致模型更新与运行环境不匹配。本文提出Co-Harness框架，实现框架与模型参数的联合优化。该框架交替进行框架优化与模型优化：基于LLM的HarnessCritic分析失败轨迹，识别框架级故障模式并提出经验证的局部更新；随后模型在由改进框架生成的高质量轨迹上微调，将有效结构融入参数。一次超过200小时的自主案例研究显示，Co-Harness可自动恢复系统崩溃、提升推理效率，并发现无需人工干预的集成策略。结果表明，联合优化是超越固定框架后训练的有效途径。
原文摘要 · Abstract (English)
Post-training agents for automated AI research requires optimizing not only model parameters, but also the runtime harness that shapes how research trajectories are generated, evaluated, and learned from. Existing pipelines typically train models under a fixed harness, including prompts, tools, skills, middleware, and memory, while leaving the data-generating process outside the optimization objective. This creates a mismatch between model updates and the static scaffolding that determines trajectory quality. We introduce Co-Harness, a framework that jointly optimizes the agent harness and model parameters during post-training. Co-Harness alternates between harness optimization and model optimization. An LLM-based HarnessCritic analyzes failed trajectories, identifies harness-level failure modes, and proposes validated local updates. The model is then fine-tuned on high-quality trajectories generated by the improved harness, distilling effective scaffolding into model parameters. A 200+ hour autonomous case study further shows that Co-Harness can recover from system crashes, improve inference efficiency, and discover ensemble strategies without human intervention. These results suggest that joint harness and model optimization is an effective way to improve agents beyond fixed-harness post-training.
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