@inproceedings{yan-etal-2026-openskill,
title = "{O}pen{S}kill: Open-World Self-Evolution for {LLM} Agents",
author = "Yan, Zhiling and
Song, Dingjie and
Zhang, Hanrong and
Liang, Wei and
Zhang, Yuxuan and
Dai, Yutong and
He, Lifang and
Yu, Philip S. and
Xu, Ran and
Li, Xiang and
Sun, Lichao",
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.1118/",
pages = "25602--25618",
ISBN = "979-8-89176-459-0",
abstract = "Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signals. Real open-world deployments may provide none of these, offering only a task prompt. In this work, we study *open-world self-evolution*, where an agent must build both its skills and its own verification signals from scratch, using open-world resources but no target-task supervision. We propose **OpenSkill**, a framework that bootstraps this loop: it acquires grounded knowledge and verification anchors from documentation, repositories, and the web, synthesizes them into transferable skills, and refines those skills against self-built virtual tasks grounded in the anchors rather than in target answers. The open world thus supplies both the knowledge to be learned and a supervision-independent practice environment, with target-task supervision reserved for final evaluation. Across three benchmarks and two target agents, OpenSkill attains the best automated pass rate while satisfying the no-supervision constraint. Analysis shows its skills transfer across models without model-specific adaptation, and its self-built verifier aligns with ground-truth outcomes despite never accessing them."
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<abstract>Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signals. Real open-world deployments may provide none of these, offering only a task prompt. In this work, we study *open-world self-evolution*, where an agent must build both its skills and its own verification signals from scratch, using open-world resources but no target-task supervision. We propose **OpenSkill**, a framework that bootstraps this loop: it acquires grounded knowledge and verification anchors from documentation, repositories, and the web, synthesizes them into transferable skills, and refines those skills against self-built virtual tasks grounded in the anchors rather than in target answers. The open world thus supplies both the knowledge to be learned and a supervision-independent practice environment, with target-task supervision reserved for final evaluation. Across three benchmarks and two target agents, OpenSkill attains the best automated pass rate while satisfying the no-supervision constraint. Analysis shows its skills transfer across models without model-specific adaptation, and its self-built verifier aligns with ground-truth outcomes despite never accessing them.</abstract>
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%0 Conference Proceedings
%T OpenSkill: Open-World Self-Evolution for LLM Agents
%A Yan, Zhiling
%A Song, Dingjie
%A Zhang, Hanrong
%A Liang, Wei
%A Zhang, Yuxuan
%A Dai, Yutong
%A He, Lifang
%A Yu, Philip S.
%A Xu, Ran
%A Li, Xiang
%A Sun, Lichao
%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 yan-etal-2026-openskill
%X Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signals. Real open-world deployments may provide none of these, offering only a task prompt. In this work, we study *open-world self-evolution*, where an agent must build both its skills and its own verification signals from scratch, using open-world resources but no target-task supervision. We propose **OpenSkill**, a framework that bootstraps this loop: it acquires grounded knowledge and verification anchors from documentation, repositories, and the web, synthesizes them into transferable skills, and refines those skills against self-built virtual tasks grounded in the anchors rather than in target answers. The open world thus supplies both the knowledge to be learned and a supervision-independent practice environment, with target-task supervision reserved for final evaluation. Across three benchmarks and two target agents, OpenSkill attains the best automated pass rate while satisfying the no-supervision constraint. Analysis shows its skills transfer across models without model-specific adaptation, and its self-built verifier aligns with ground-truth outcomes despite never accessing them.
%U https://aclanthology.org/2026.emnlp-main.1118/
%P 25602-25618
Markdown (Informal)
[OpenSkill: Open-World Self-Evolution for LLM Agents](https://aclanthology.org/2026.emnlp-main.1118/) (Yan et al., EMNLP 2026)
ACL
- Zhiling Yan, Dingjie Song, Hanrong Zhang, Wei Liang, Yuxuan Zhang, Yutong Dai, Lifang He, Philip S. Yu, Ran Xu, Xiang Li, and Lichao Sun. 2026. OpenSkill: Open-World Self-Evolution for LLM Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 25602–25618, Budapest, Hungary. Association for Computational Linguistics.