用图结构推理技能组合，让大模型更高效完成复杂任务
SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents
- 构建查询-技能图，分层建模语义、匹配与依赖关系
- 在SkillsBench上达53.17%成功率，ALFWorld上91.43%
- 适配多种大模型，提升技能组合的通用性与鲁棒性
大型语言模型代理通过组合技能库中的可复用技能来解决复杂任务。核心挑战不仅在于检索单个相关技能，更在于识别完整且可执行的技能组合。本文提出SkillTrace，构建包含三层关系的图结构：技能查询间的组合关系、查询与技能候选之间的相似性、以及所选候选间的依赖关系。该方法将用户查询组织为语义层级，匹配技能查询与候选，并在技能依赖图上传播信息。在SkillsBench和ALFWorld上的实验表明，SkillTrace达到当前最佳性能，在SkillsBench上成功率达53.17%，在ALFWorld上达91.43%。其表现对不同基础语言模型具有一致提升，验证了基于图的技能检索方法的通用性与鲁棒性。
原文摘要 · Abstract (English)
Large language model agents increasingly solve complex tasks by composing reusable skills from a library. To address this, the key challenge is not merely to retrieve individually relevant skills, but to identify a complete and executable skill composition. In this paper, we argue that this problem can be solved in a graph with three levels: compositional relations among skill queries, similarity between queries and candidates in the skill library, and the dependencies among the selected candidates. We introduce SkillTrace, which organizes the user query into a semantic hierarchy, matches skill queries and candidates, and propagates over the skill dependencies. Experiments on SkillsBench and ALFWorld demonstrate that SkillTrace achieves state-of-the-art performance, reaching a success rate of 53.17% on SkillsBench and 91.43% on ALFWorld. SkillTrace also delivers consistent improvements across different backbone language models, demonstrating the generality and robustness of graph-based skill retrieval.
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