Paper Detail
Wenbo Pan, Shujie Liu, Chin-Yew Lin, Jingying Zeng, Xianfeng Tang, Xiangyang Zhou, Yan Lu, Xiaohua Jia
AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to new tasks. However, existing optimization methods typically require ground-truth validation sets, yet such labeled data is difficult to acquire in practical deployment settings. To address this problem, we introduce Retrospective Harness Optimization (RHO), a self-supervised method that optimizes the agent harness using only past trajectories. Specifically, RHO selects a diverse coreset of challenging tasks from past trajectories and re-solves them in parallel. The agent analyzes these rollouts using self-validation and self-consistency, then generates candidate harness updates and selects the most effective one by its own pairwise self-preference. We evaluate RHO across three diverse domains, spanning software engineering, technical work, and knowledge work. Notably, a single optimization round improves the pass rate on SWE-Bench Pro from 59% to 78% without any external grading. Furthermore, our analysis demonstrates that RHO effectively targets prior failure modes. As a result, the optimized harness alters the agent's behavior patterns and sustains higher accuracy during long-horizon sessions.
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@misc{pan2026retrospective,
title = {Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts},
author = {Wenbo Pan and Shujie Liu and Chin-Yew Lin and Jingying Zeng and Xianfeng Tang and Xiangyang Zhou and Yan Lu and Xiaohua Jia},
year = {2026},
abstract = {AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to new tasks. However, existing optimization methods typically require ground-truth validation sets, yet such labeled data is difficult to acquire in practical deployment settings. To address this problem, we introduce Retrospective Harness Optimization (RHO), a self-supervised method that optimizes the agent harness using only past trajectories. Spe},
url = {https://huggingface.co/papers/2606.05922},
keywords = {Retrospective Harness Optimization, self-supervised method, agent harness, past trajectories, coreset, parallel re-solving, self-validation, self-consistency, pairwise self-preference, SWE-Bench Pro, code available, huggingface daily},
eprint = {2606.05922},
archiveprefix = {arXiv},
}
{}