From Trajectories to Differentiated Skills: Cognitive Differentiation for Self-Improving Agents· 从轨迹到差异化技能：自我改进代理的认知分化
Large language model (LLM) agents tackle complex tasks through reasoning and interaction with external environments. Agent skills provide persistent, transferable capabilities that guide task planning, inform contextual decisions, support tool use, and enable executable procedures. Yet existing skill-evolution methods largely organize knowledge around skill artifacts, overlooking the functional heterogeneity within individual skills and its implications for knowledge maintenance and use. Inspired by cognitive science, we propose Cognitive Differentiation in Skill Evolution (CDSE), which evolves a unified skill through four functionally distinct yet coordinated components: Meta, Experience, Tool, and Script. CDSE extracts evidence-grounded semantic gradients from successful and failed traje
提出基于认知科学的自我改进代理技能演化新方法，强调技能内部功能多样性。
- 核心方法
- 通过四个功能不同的协调组件（元认知、经验、工具和脚本）来演化统一的技能，提取成功和失败轨迹中的证据支持的语义梯度。
- 适合谁读
- 研究者
- 要解决的问题
- 现有技能演化方法忽略了个体技能内部功能的异质性，导致知识维护和使用上的局限。
- 关键实验
- 未提供
- 主要贡献
- 引入认知分化技能演化（CDSE）框架，提升代理在复杂任务中的自我改进和技能转移能力。
- 意义与局限
- 该方法有助于构建更加高效和自适应的AI代理，但需要进一步实验验证其在实际任务中的表现。