@inproceedings{pan-etal-2026-evolving,
title = "Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference",
author = "Pan, Wenbo and
Liu, Shujie and
Lin, Chin-Yew and
Zeng, Jingying and
Tang, Xianfeng and
Zhou, Xiangyang and
Lu, Yan and
Jia, Xiaohua",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2439/",
pages = "52321--52362",
ISBN = "979-8-89176-462-0",
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. 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. Code is available at https://github.com/wbopan/retro-harness and the project website at https://paper-rho.wenbo.io."
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<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. 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. Code is available at https://github.com/wbopan/retro-harness and the project website at https://paper-rho.wenbo.io.</abstract>
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%0 Conference Proceedings
%T Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference
%A Pan, Wenbo
%A Liu, Shujie
%A Lin, Chin-Yew
%A Zeng, Jingying
%A Tang, Xianfeng
%A Zhou, Xiangyang
%A Lu, Yan
%A Jia, Xiaohua
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F pan-etal-2026-evolving
%X 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. Code is available at https://github.com/wbopan/retro-harness and the project website at https://paper-rho.wenbo.io.
%U https://aclanthology.org/2026.findings-emnlp.2439/
%P 52321-52362
Markdown (Informal)
[Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference](https://aclanthology.org/2026.findings-emnlp.2439/) (Pan et al., Findings 2026)
ACL
- Wenbo Pan, Shujie Liu, Chin-Yew Lin, Jingying Zeng, Xianfeng Tang, Xiangyang Zhou, Yan Lu, and Xiaohua Jia. 2026. Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 52321–52362, Budapest, Hungary. Association for Computational Linguistics.