NaviAgent: Graph-Driven Bilevel Planning for Scalable Tool Orchestration

TL;DR

NaviAgent introduces a graph-based bilevel architecture for scalable, robust tool invocation in LLMs, improving task success and efficiency in large tool ecosystems.

Contribution

It proposes a novel graph-driven bilevel framework with a Tool World Navigation Model for improved scalability and robustness in tool orchestration.

Findings

Achieves a 13.1 point increase in task success rate on complex tasks.

Demonstrates consistent 4.3-12.0 point gains across real APIs and domains.

Reduces steps and latency in tool invocation sequences.

Abstract

Large Language Models (LLMs) increasingly act as function-call agents that invoke external tools to tackle tasks beyond their static knowledge. However, they typically invoke tools one at a time without a global view of task structure. As tools often depend on one another, this leads to error accumulation and poor scalability, particularly when scaling to hundreds or thousands of tools. To address these limitations, we propose NaviAgent, an explicit bilevel architecture that decouples task planning from tool execution through graph-based modeling of tool relations. At the planning level, the LLM-based agent decides whether to respond directly, clarify intent, or retrieve and execute a toolchain independent of inter-tool complexity. At the execution level, a Tool World Navigation Model (TWNM) encodes structural and behavioral relations among tools, steering the agent to compose scalable and robust invocation sequences. Incorporating feedback from real tool interactions, NaviAgent achieves closed-loop alignment between planning and execution, enabling adaptive navigation in large-scale tool ecosystems. Evaluations on API-Bank and ToolBench show consistent improvements in task success rate (TSR), with TWNM yielding an average gain of 13.1 points on complex tasks. Further tests on 50 real APIs across 7 domains show consistent gains of 4.3--12.0 points, with fewer steps and latency, demonstrating robust generalization under real-world dynamics.