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

arXiv 2510.24690 ↗First posted 28 Oct 2025cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 28 Oct 2025.

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References · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  3. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  4. 2025From RAG to Memory: Non-Parametric Continual Learning for Large Language Modelscited by 231
  5. 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Taskscited by 195
  6. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 184
  7. 2024jina-embeddings-v3: Multilingual Embeddings With Task LoRAcited by 141
  8. 2024Tool-Planner: Task Planning with Clusters across Multiple Toolscited by 33
  9. 2025Improving Large Language Model Planning with Action Sequence Similaritycited by 7

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