这项研究揭示了LLM智能体技能迁移的关键因素，通过技能效用分数评估技能记忆，对理解智能体技能提升有重要意义。
大型语言模型（LLM）智能体能从完成任务中诱导技能并重复使用，但技能迁移可能不可靠甚至损害智能体。本研究比较了任务级和子任务级技能诱导以及文本和代码技能格式，发现子任务级技能平均提升智能体表现，文本技能迁移效果优于代码技能。研究还提出技能效用分数，结合具体性和抽象性两个属性，预测任务成功。该分数可作为技能记忆的实用诊断工具。
Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents
Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience. In practice, induced skills may transfer unreliably and can even harm the agent that retrieves them. When agent-induced skills transfer reliably across tasks remains an open question. We conduct a comprehensive and controlled study of how the way skills are induced shapes their transfer across tasks. Specifically, we compare task-level with subtask-level skill induction and text with code skill formats, the two axes along which existing methods differ. Task-level skills mostly reduce the agent's performance below its no-memory baseline while subtask-level skills raise it above on average, and text skills transfer better than code skills. To further understand our findings, we examine two complementary properties of the induced skills: specificity, which measures how closely a skill matches real tasks, and abstractness, which measures how evenly its relevance spreads across tasks. Neither property alone predicts task success, but their combined effect does, which we propose as a skill utility score. The score correlates consistently with task success when skills are transferred, and subtask-level and text skills score higher. Computing skill utility only needs the skills and task descriptions but not any task execution, so our score serves as a practical diagnostic of a skill memory before any new task runs.