arXiv:2605.27366v1 Announce Type: cross 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. 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
The rapid advancement of large language models is leading to increased research in how to make these agents more autonomous and capable of continuous self-improvement.
This research outlines a pathway for AI agents to move beyond static, handcrafted skills, enabling more robust, adaptable, and long-term autonomous operation that can impact white-collar workflows.
AI agents are evolving from merely executing pre-defined tasks to continuously learning, creating, and refining their own operational skills, enhancing their utility and generalization capabilities.
- · AI software developers
- · Enterprises adopting AI agents
- · Research institutions
- · Tasks requiring repetitive, static skill execution
- · Traditional, non-adaptive automation platforms
More sophisticated and reliable AI agents become viable for widespread deployment across various industries.
The increasing autonomy of AI agents could lead to significant restructuring of human-computer interaction paradigms and job functions.
Self-evolving skill sets in AI agents might accelerate the development of truly general AI, profoundly impacting economic and social structures.
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