@inproceedings{wei-etal-2026-evo,
title = "Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents",
author = "Wei, Tianxin and
Shi, Zhan and
Lin, Minhua and
He, Bing and
Liu, Zewen and
Sang, Yisi and
Bei, Yuanchen and
Ning, Xuying and
Zou, Jiaru and
Li, Ting-Wei and
Lin, Xiao and
Zhao, Yanjun and
Wang, Chi and
Dumoulin, Benoit and
Wang, Dakuo and
He, Jingrui and
Lu, Hanqing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.969/",
pages = "22343--22358",
ISBN = "979-8-89176-459-0",
abstract = "Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically extract knowledge from accumulated trajectories via reflection, memory, rules, or skills. However, agents in realistic environments continuously encounter novel tasks, often offering only a one-shot opportunity to improve. These executions yield rich but highly noisy contexts, entangling broadly useful lessons with task-specific artifacts. Critically, prior works rarely validate their effectiveness on complex real-world tasks or isolate the underlying drivers of improvement. To address these gaps, we study online harness learning, where a frozen agent improves by continually updating a structured harness across sequential tasks. This setup enables a systematic study of key self-improvement factors through our proposed Evo-Harness. At its core, context-to-harness skill compilation distills noisy, single-shot executions into reusable skill harnesses for cross-domain and topic-level adaptation. To demonstrate the efficacy of one-shot skill compilation, we evaluate across five realistic benchmarks (TerminalBench2, SWE-bench, CL-Bench, $\tau$-bench, WebArena-Infinity). Our extensive analysis demonstrates the effectiveness of Evo-Harness and provides a principled understanding of how LLM agents can effectively learn on the fly. Our code is available at https://github.com/A-EVO-Lab/a-evolve/tree/release/evo-harness."
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<abstract>Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically extract knowledge from accumulated trajectories via reflection, memory, rules, or skills. However, agents in realistic environments continuously encounter novel tasks, often offering only a one-shot opportunity to improve. These executions yield rich but highly noisy contexts, entangling broadly useful lessons with task-specific artifacts. Critically, prior works rarely validate their effectiveness on complex real-world tasks or isolate the underlying drivers of improvement. To address these gaps, we study online harness learning, where a frozen agent improves by continually updating a structured harness across sequential tasks. This setup enables a systematic study of key self-improvement factors through our proposed Evo-Harness. At its core, context-to-harness skill compilation distills noisy, single-shot executions into reusable skill harnesses for cross-domain and topic-level adaptation. To demonstrate the efficacy of one-shot skill compilation, we evaluate across five realistic benchmarks (TerminalBench2, SWE-bench, CL-Bench, τ-bench, WebArena-Infinity). Our extensive analysis demonstrates the effectiveness of Evo-Harness and provides a principled understanding of how LLM agents can effectively learn on the fly. Our code is available at https://github.com/A-EVO-Lab/a-evolve/tree/release/evo-harness.</abstract>
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%0 Conference Proceedings
%T Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents
%A Wei, Tianxin
%A Shi, Zhan
%A Lin, Minhua
%A He, Bing
%A Liu, Zewen
%A Sang, Yisi
%A Bei, Yuanchen
%A Ning, Xuying
%A Zou, Jiaru
%A Li, Ting-Wei
%A Lin, Xiao
%A Zhao, Yanjun
%A Wang, Chi
%A Dumoulin, Benoit
%A Wang, Dakuo
%A He, Jingrui
%A Lu, Hanqing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wei-etal-2026-evo
%X Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically extract knowledge from accumulated trajectories via reflection, memory, rules, or skills. However, agents in realistic environments continuously encounter novel tasks, often offering only a one-shot opportunity to improve. These executions yield rich but highly noisy contexts, entangling broadly useful lessons with task-specific artifacts. Critically, prior works rarely validate their effectiveness on complex real-world tasks or isolate the underlying drivers of improvement. To address these gaps, we study online harness learning, where a frozen agent improves by continually updating a structured harness across sequential tasks. This setup enables a systematic study of key self-improvement factors through our proposed Evo-Harness. At its core, context-to-harness skill compilation distills noisy, single-shot executions into reusable skill harnesses for cross-domain and topic-level adaptation. To demonstrate the efficacy of one-shot skill compilation, we evaluate across five realistic benchmarks (TerminalBench2, SWE-bench, CL-Bench, τ-bench, WebArena-Infinity). Our extensive analysis demonstrates the effectiveness of Evo-Harness and provides a principled understanding of how LLM agents can effectively learn on the fly. Our code is available at https://github.com/A-EVO-Lab/a-evolve/tree/release/evo-harness.
%U https://aclanthology.org/2026.emnlp-main.969/
%P 22343-22358
Markdown (Informal)
[Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents](https://aclanthology.org/2026.emnlp-main.969/) (Wei et al., EMNLP 2026)
ACL
- Tianxin Wei, Zhan Shi, Minhua Lin, Bing He, Zewen Liu, Yisi Sang, Yuanchen Bei, Xuying Ning, Jiaru Zou, Ting-Wei Li, Xiao Lin, Yanjun Zhao, Chi Wang, Benoit Dumoulin, Dakuo Wang, Jingrui He, and Hanqing Lu. 2026. Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 22343–22358, Budapest, Hungary. Association for Computational Linguistics.