MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
The MUSE-Autoskill framework introduces a new approach for self-evolving agents that enhances their ability to create and manage skills. This framework allows agents to continuously improve their task-solving capabilities through a unified lifecycle of skill management. Initial experiments suggest that this method can significantly enhance task success and efficiency.
- ▪MUSE-Autoskill is a skill-centric agent framework designed for continuous improvement in task-solving capabilities.
- ▪The framework enables agents to create, reuse, and refine skills while managing them through a unified lifecycle.
- ▪Experiments indicate that lifecycle-managed skills can improve task success, efficiency, and cross-agent transfer.
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Computer Science > Artificial Intelligence arXiv:2605.27366 (cs) [Submitted on 26 May 2026] 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. However, existing skill creation approaches treat skills as isolated and static artifacts, limiting their reusability, reliability, and long-term improvement.
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