SkillGraph: Graph Foundation Priors for LLM Agent Tool Sequence Recommendation

📰 ArXiv cs.AI

Learn how SkillGraph improves LLM agent tool sequence recommendation using graph foundation priors, enhancing workflow efficiency

advanced Published 23 Apr 2026
Action Steps
  1. Build a SkillGraph using a directed weighted execution-transition graph
  2. Mine successful workflows to inform the graph structure
  3. Apply graph foundation priors to improve tool sequence recommendation
  4. Evaluate the performance of SkillGraph using metrics like Kendall-$ au$
  5. Integrate SkillGraph into an LLM agent to enhance workflow efficiency
Who Needs to Know This

This benefits AI engineers and researchers working on LLM agents, as it enhances their ability to recommend tool sequences and improve workflow efficiency. The team can apply this knowledge to develop more effective LLM agents

Key Insight

💡 SkillGraph uses graph foundation priors to capture inter-tool data dependencies, outperforming semantic-only methods

Share This
💡 Improve LLM agent workflow efficiency with SkillGraph, a graph-based approach to tool sequence recommendation

Key Takeaways

Learn how SkillGraph improves LLM agent tool sequence recommendation using graph foundation priors, enhancing workflow efficiency

Full Article

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

Abstract:
arXiv:2604.19793v1 Announce Type: new Abstract: 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-$\tau$ in structured workflow domains. We introduce SkillGraph, a directed weighted execution-transition graph mined from 49,831 successful L
Read full paper → ← Back to Reads

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