SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents
2026-08-03 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors address the problem of solving complex tasks with language model agents by not just finding individual skills but combining them correctly. They propose SkillTrace, a method that builds a graph connecting user requests, skill library matches, and their dependencies. By organizing queries hierarchically and using this graph, SkillTrace helps pick and link skills more accurately. Tests show it performs better than previous methods across different setups, proving its effectiveness and flexibility.
large language modelsskill compositiongraph-based retrievalsemantic hierarchyskill dependenciesSkillsBenchALFWorldlanguage model agents
Authors
Yue Yao, Shengyuan Wang, Xin Chen, Minke Zhang, Jia He, Bingjun Luo, Tom Gedeon
Abstract
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.