Bridging Tool Dependencies and Domain Knowledge: A Graph-Based Framework for In-Context Planning

Shengjie Liu, Li Dong, Zhenyu Zhang

Keywords: Knowlege Graph, In Context Planning

TL;DR: We propose a unified framework that fuses tool and document knowledge graphs to capture dependencies and enhance exemplar plan generation, effectively modeling tool interactions and improving tool-augmented reasoning.

Abstract:

We present a framework for uncovering and exploiting dependencies among tools
and documents to enhance exemplar artifact generation. Our method begins by
constructing a tool knowledge graph from tool schemas—including descriptions,
arguments, and output payloads—using a DeepResearch-inspired analysis. In
parallel, we derive a complementary knowledge graph from internal documents
and SOPs, which is then fused with the tool graph. To generate exemplar plans, we
adopt a deep–sparse integration strategy that aligns structural tool dependencies
with procedural knowledge. Experiments demonstrate that this unified framework
effectively models tool interactions and improves plan generation, underscoring the
benefits of linking tool graphs with domain knowledge graphs for tool-augmented
reasoning and planning.

Submission Type: Research Paper (4-9 Pages)

Submission Number: 79