Selm: Self-Evolving Library Management for Multi-Skill Agent Optimization
Large language model (LLM) agents increasingly use skills as a lightweight way to acquire task-specific knowledge. Recent work enables skills to evolve from task trajectories. However, these methods often lack systematic management of evolved skills, making it difficult for agents to reuse and accumulate skills across domains, thus limiting performance on subsequent tasks.To reliably accumulate, reuse, and maintain useful skills across tasks, we present SELM (Self-Evolving Library Management), a framework that formulates skill evolution as library-level management. SELM jointly manages skill retrieval, updating, and lifecycle by maintaining skill confidence and utility signals. It introdu...
| Authors | Mengxi Hu, Yuxuan Hu, Jinbo Su et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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