ToolTree: Efficient LLM Agent Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning

2026-03-13

AI Summary

This work addresses the limitation of existing large language model (LLM) agents in multi-step tasks, which often employ short-sighted greedy strategies for tool selection and lack global planning capabilities that account for tool dependencies. To overcome this, the authors propose ToolTree, a Monte Carlo tree search-inspired framework for tool planning. ToolTree incorporates a two-stage LLM evaluation process and a bidirectional pruning mechanism applied before and after execution, enabling efficient yet forward-looking exploration of tool usage trajectories. The method explicitly models inter-tool dependencies to support multi-step reasoning and planning. Experimental results demonstrate that ToolTree achieves state-of-the-art performance on both open-set and closed-set tool planning tasks, with an average improvement of approximately 10% across four benchmark datasets.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Model (LLM) agents are increasingly applied to complex, multi-step tasks that require interaction with diverse external tools across various domains. However, current LLM agent tool planning methods typically rely on greedy, reactive tool selection strategies that lack foresight and fail to account for inter-tool dependencies. In this paper, we present ToolTree, a novel Monte Carlo tree search-inspired planning paradigm for tool planning. ToolTree explores possible tool usage trajectories using a dual-stage LLM evaluation and bidirectional pruning mechanism that enables the agent to make informed, adaptive decisions over extended tool-use sequences while pruning less promising branches before and after the tool execution. Empirical evaluations across both open-set and closed-set tool planning tasks on 4 benchmarks demonstrate that ToolTree consistently improves performance while keeping the highest efficiency, achieving an average gain of around 10\% compared to the state-of-the-art planning paradigm.

Problem

• LLM agent
• tool planning
• multi-step tasks
• tool dependencies
• greedy strategy

Innovation

• Monte Carlo Tree Search
• LLM Agent
• Tool Planning
• Bidirectional Pruning
• Dual-Feedback Evaluation