SkillX: Automatically Constructing Skill Knowledge Bases for Agents
Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited experience, resulting in redundant exploration and poor generalization. To address this problem, we propose SkillX, a fully automated framework for constructing a \textbf{plug-and-play skill knowledge base} that can be reused across agents and environments. SkillX operates through a fully automated pipeline built on three synergistic innovations: \textit{(i) Multi-Level Skills Design}, which distills raw trajectories into three-tiered hierarchy of strategic pla...
| Authors | Chenxi Wang, Zhuoyun Yu, Xin Xie et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 April 2026 |
| Last updated at source | 19 April 2026 |
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