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

2025-10-28

AI Summary

This work addresses inaccurate planning in tool invocation caused by the absence of explicit dependency modeling. We propose a dual-graph collaborative framework integrating a tool knowledge graph with a domain-specific document knowledge graph. Methodologically: (1) we automatically construct a tool dependency graph from tool schemas; (2) we extract procedural knowledge from SOP documents to build a domain workflow graph; and (3) we introduce a novel deep sparse ensemble strategy to align and fuse the two graphs at both structural and semantic levels. Our key contribution lies in explicitly modeling implicit tool dependencies and organically coupling tool capabilities with domain workflow logic. Experiments demonstrate that our framework significantly improves planning rationality, tool invocation accuracy, and contextual consistency in example artifact generation, outperforming existing tool-augmented reasoning methods across multiple benchmark tasks.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Modeling tool dependencies and domain knowledge integration
• Generating exemplar plans through graph-based framework
• Improving tool-augmented reasoning via structured knowledge fusion

Innovation

• Constructs tool knowledge graph from schemas
• Fuses tool graph with document knowledge graph
• Aligns tool dependencies with procedural knowledge