AGENTIC-SYSTEMSApril 23, 20261 min read8 views

SkillGraph: Graph Foundation Priors for LLM Agent Tool Sequence Recommendation

SkillGraph introduces a directed weighted execution-transition graph mined from nearly 50,000 successful trajectories to improve tool selection and ordering for LLM agents. It outperforms existing semantic-only methods and large models like LLaMA-3.1-8B in structured workflow tasks.

Computer Science > Artificial Intelligence

Title:SkillGraph: Graph Foundation Priors for LLM Agent Tool Sequence Recommendation

LLM agents must select tools from large API libraries and order them correctly. Existing methods use semantic similarity for both retrieval and ordering, but ordering depends on inter-tool data dependencies that are absent from tool descriptions. As a result, semantic-only methods can produce negative Kendall-τ in structured workflow domains. We introduce SkillGraph, a directed weighted execution-transition graph mined from 49,831 successful LLM agent trajectories, which encodes workflow-precedence regularities as a reusable graph foundation prior. Building on this graph foundation prior, we propose a two-stage decoupled framework: GS-Hybrid retrieval for candidate selection and a learned pairwise reranker for ordering. On ToolBench (9,965 test instances; ~16,000 tools), the method reaches Set-F1 = 0.271 and Kendall-τ = 0.096; on API-Bank, Kendall-τ improves from -0.433 to +0.613. Under identical Stage-1 inputs, the learned reranker also outperforms LLaMA-3.1-8B Stage-2 rerankers.

Source: arXiv cs.AI Recent

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