Dingxuan Zhang, Yanhui Jiang, Jun Wang, Hefeng Zhou · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31461 · open-access PDF
可重复使用的技能允许大型语言模型代理人将任务特定的程序、工具辅助和执行指导包装成模块化单元。以前的工作主要研究了代理人在执行前如何获取、编写或合成技能。在编码代理人中,执行会带来额外的运行时间控制问题:同一任务可以通过原始工具调用、宏观技能或混合执行来解决,该任务从宏观开始,再回到微级恢复。
Machine translation (Meta M2M100); the English original follows.
Reusable skills allow large language model agents to package task-specific procedures, tool affordances, and execution guidance into modular units. Prior work has largely studied how agents retrieve, compose, or synthesize skills before execution. In coding agents, however, execution poses an additional runtime control problem: the same task may be solved through primitive tool calls, macro skills, or hybrid execution that starts with a macro and falls back to micro-level recovery. We introduce Adaptive Skill Granularity, a framework that treats skill granularity as an action-abstraction polic
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