SIT-Graph: State Integrated Tool Graph for Multi-Turn Agents

2025-12-08

AI Summary

In multi-turn tool-use scenarios, agents face dual challenges: progressive intent clarification and dynamic environmental evolution; existing approaches—relying rigidly on complete trajectories or fixed subtasks—struggle to adapt to temporal changes in state and information. This paper proposes the State-Integrated Tool Graph (SIT-Graph), the first framework unifying episodic memory (compressed state summaries) and procedural memory (tool dependency structures) to construct a dynamically retrievable, state-aware tool graph. During inference, SIT-Graph integrates historical sequence modeling, retrieval augmentation, and high-confidence path matching to enable context-driven, recall-execution co-decision making. Evaluated on multiple stateful, multi-turn tool-use benchmarks, it significantly outperforms strong baselines, improving both tool-selection robustness and cross-task experience transfer efficiency.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Graph-based Machine Learning

Application Category

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

Abstract

Despite impressive advances in agent systems, multi-turn tool-use scenarios remain challenging. It is mainly because intent is clarified progressively and the environment evolves with each tool call. While reusing past experience is natural, current LLM agents either treat entire trajectories or pre-defined subtasks as indivisible units, or solely exploit tool-to-tool dependencies, hindering adaptation as states and information evolve across turns. In this paper, we propose a State Integrated Tool Graph (SIT-Graph), which enhances multi-turn tool use by exploiting partially overlapping experience. Inspired by human decision-making that integrates episodic and procedural memory, SIT-Graph captures both compact state representations (episodic-like fragments) and tool-to-tool dependencies (procedural-like routines) from historical trajectories. Specifically, we first build a tool graph from accumulated tool-use sequences, and then augment each edge with a compact state summary of the dialog and tool history that may shape the next action. At inference time, SIT-Graph enables a human-like balance between episodic recall and procedural execution: when the next decision requires recalling prior context, the agent retrieves the state summaries stored on relevant edges and uses them to guide its next action; when the step is routine, it follows high-confidence tool dependencies without explicit recall. Experiments across multiple stateful multi-turn tool-use benchmarks show that SIT-Graph consistently outperforms strong memory- and graph-based baselines, delivering more robust tool selection and more effective experience transfer.

Problem

• Enhances multi-turn tool use by exploiting partially overlapping experience
• Captures compact state representations and tool-to-tool dependencies from historical trajectories
• Enables a balance between episodic recall and procedural execution for robust tool selection

Innovation

• State Integrated Tool Graph captures episodic and procedural memory
• Graph edges store compact state summaries from dialog history
• Balances episodic recall with procedural execution for tool selection