# MUSE-Autoskill：通过技能创建、记忆、管理与评估实现智能体的自我进化

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-26 08:00
- AIHOT 分数：57
- AIHOT 链接：https://aihot.virxact.com/items/cmpnfrc8m0x76sl01htn2t8oa
- 原文链接：https://arxiv.org/abs/2605.27366

## AI 摘要

MUSE-Autoskill 提出了一个以技能为核心的智能体框架，使智能体能够通过统一的技能生命周期（创建、记忆、管理、评估与精炼）持续提升任务解决能力。该框架支持按需创建技能、跨任务存储与重用，并借助单元测试和运行时反馈进行持续改进。它还引入了技能级记忆，用于为每个技能积累跨任务经验。在 SkillsBench 上的初步实验表明，经过生命周期管理的技能可以提升任务成功率、效率、重用性及跨智能体迁移能力，突显了将技能作为长期、可感知经验且可测试的资产的重要性。

## 正文

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.