{"slug": "muse-autoskill-self-evolving-agents-via-skill-creation-and-memory", "title": "Muse-Autoskill: Self-Evolving Agents via Skill Creation and Memory", "summary": "Researchers have developed MUSE-Autoskill, a framework that enables large language model agents to continuously improve their task-solving abilities by creating, reusing, and refining skills through a unified lifecycle. The system introduces skill-level memory that accumulates experience across tasks, allowing agents to adapt and transfer skills more effectively. Initial tests on SkillsBench showed that lifecycle-managed skills improved task success, efficiency, reuse, and cross-agent transfer, positioning skills as long-lived, experience-aware assets rather than static artifacts.", "body_md": "# Computer Science > Artificial Intelligence\n\n[Submitted on 26 May 2026]\n\n# Title:MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation\n\n[View PDF](/pdf/2605.27366)\n\n[HTML (experimental)](https://arxiv.org/html/2605.27366v1)\n\nAbstract: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. We propose MUSE-Autoskill Agent (Memory-Utilizing Skill Evolution), a skill-centric agent framework that lets agents continuously improve their task-solving capability by creating, reusing, and refining skills under a unified lifecycle (creation, memory, management, evaluation, and refinement). Our framework enables agents to create skills on demand, store and reuse them across tasks, organize and select them efficiently, and evaluate them through unit tests and runtime feedback for continuous refinement. We further introduce skill-level memory that accumulates experience for each skill across tasks, enabling more effective reuse and adaptation over time. Experiments on SkillsBench provide initial evidence that lifecycle-managed skills can improve task success, efficiency, reuse, and cross-agent transfer, highlighting the importance of treating skills as long-lived, experience-aware, and testable assets.\n\n### Current browse context:\n\ncs.AI\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/muse-autoskill-self-evolving-agents-via-skill-creation-and-memory", "canonical_source": "https://arxiv.org/abs/2605.27366", "published_at": "2026-05-28 12:23:34+00:00", "updated_at": "2026-05-28 12:29:02.284221+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents"], "entities": ["MUSE-Autoskill", "SkillsBench"], "alternates": {"html": "https://wpnews.pro/news/muse-autoskill-self-evolving-agents-via-skill-creation-and-memory", "markdown": "https://wpnews.pro/news/muse-autoskill-self-evolving-agents-via-skill-creation-and-memory.md", "text": "https://wpnews.pro/news/muse-autoskill-self-evolving-agents-via-skill-creation-and-memory.txt", "jsonld": "https://wpnews.pro/news/muse-autoskill-self-evolving-agents-via-skill-creation-and-memory.jsonld"}}