自主進化智能體技能管理

Computer Science > Artificial Intelligence arXiv:2605.

27366 (cs) [Submitted on 26 May 2026 (v1), last revised 3 Jul 2026 (this version, v2)] Title:MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation Authors:Huawei Lin, Peng Li, Jie Song, Fuxin Jiang, Tieying Zhang View a PDF of the paper titled MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation, by Huawei Lin and 4 other authors View PDF HTML (experimental) Abstract:Large language model (LLM) agents rely on reusable skills to solve complex tasks, but existing skill creation approaches often treat skills as isolated, static artifacts, limiting reusability, reliability, and long-term improvement.

We propose MUSE-Autoskill Agent (Memory-Utilizing Skill Evolution), a skill-centric agent framework that creates, reuses, and refines skills under a unified lifecycle: creation, memory, management, evaluation, and refinement.

MUSE creates skills on demand, stores them across tasks, retrieves them through a skill catalog, and accumulates per-skill experience for later reuse and adaptation.Across the main reported settings on SkillsBench and SkillLearnBench, MUSE-Autoskill outperforms Hermes, Codex, and Claude Code.

On SkillsBench, its self-created skills surpass human-authored skills on the successfully covered subset (85.24% vs.81.

17%), showing that lifecycle-managed skills can distill agent experience into highly effective reusable assets; MUSE-created skills also transfer to Hermes more effectively than Codex- or Claude-created skills, reaching 51.90% accuracy under transfer.

These results highlight the importance of treating skills as long-lived, experience-aware, and testable assets.Comments: 30 pages, 9 figures, 15 tables, Under Review Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.

MA) Cite as: arXiv:2605.27366 [cs.AI] (or arXiv:2605.27366v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2605.

27366 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Huawei Lin [view email] [v1] Tue, 26 May 2026 17:59:19 UTC (1,113 KB) [v2] Fri, 3 Jul 2026 08:22:38 UTC (1,393 KB) Full-text links: Access Paper: View a PDF of the paper titled MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation, by Huawei Lin and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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