HiSkill：利用分层技能图增强大语言模型智能体

中文摘要 AI 辅助

技能已成为使大语言模型（LLM）智能体在长期交互任务中重用过去经验的重要抽象。然而，现有的从轨迹到技能的方法通常产生独立存储和检索的高级文本技能的扁平集合，导致技能关系未得到充分利用，且高级技能与可执行动作之间存在差距。本文提出HiSkill，一种分层技能图框架，将交互轨迹组织成具有技能节点、原子操作（AtomicOp）节点和类型化边的有向图。该图连接可重用的高级技能与可执行动作模板，同时捕捉它们之间的分解、时间转换、兼容性、支持和恢复关系。在推理时，HiSkill检索紧凑的任务相关子图并执行子图引导的任务执行，其中符号任务状态、活跃技能和检索到的子图引导LLM智能体迭代地切换技能、选择原子操作并确定可执行动作。在三个交互环境上的实验表明，HiSkill在减少推理令牌消耗的同时优于现有基线，证明了通过分层技能图弥合高级技能与可执行动作基础之间差距的有效性。我们的数据和代码可在该https网址获取。

英文摘要

Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks. However, existing trajectory-to-skill methods often produce flat collections of high-level textual skills that are stored and retrieved independently, leaving skill relations underutilized and maintaining a gap between high-level skills and executable actions. In this paper, we propose HiSkill, a hierarchical skill graph framework that organizes interaction trajectories into a directed graph with skill nodes, AtomicOp nodes, and typed edges. Specifically, the graph connects reusable high-level skills with executable action templates, while also capturing decomposition, temporal transition, compatibility, support, and recovery relations among them. At inference time, HiSkill retrieves a compact task-relevant subgraph and performs subgraph-guided task execution, where a symbolic task state, an active skill, and the retrieved subgraph guide the LLM agent to switch skills, select AtomicOps, and ground executable actions iteratively. Experiments on three interactive environments show that HiSkill outperforms state-of-the-art baselines while reducing inference token consumption, demonstrating the effectiveness of bridging high-level skills and executable action grounding through a hierarchical skill graph. Our data and code is available at https://github.com/BUPT-GAMMA/HiSkill.

发表机构

• Beijing University of Posts and Telecommunications（北京邮电大学）
• China Mobile Group Shaanxi Co., Ltd（中国移动通信集团陕西有限公司）
• Ant Group（蚂蚁集团）

机构由 AI 辅助整理，请以论文原文为准。