HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking

2025-05-05

AI Summary

To address challenges in complex planning tasks—including excessively long reasoning chains, diverse constraints, and heterogeneous subtasks—this paper proposes the hypertree-structured planning paradigm. Methodologically, it adopts a divide-and-conquer strategy to enable hierarchical and dynamic reasoning: (i) it introduces the hypertree structure to model multi-granularity planning processes with constraint awareness and scalable inference; (ii) it designs an autonomous iterative refinement framework that overcomes expressivity limitations of conventional linear or tree-structured planners; and (iii) it develops an LLM-based dynamic hypertree expansion mechanism, a constraint-driven subtask decomposition and coordinated scheduling algorithm, and an iterative planning outline optimization technique. Evaluated on the TravelPlanner benchmark, our approach achieves state-of-the-art accuracy using Gemini-1.5-Pro, outperforming o1-preview by 3.6× in planning performance.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathematical and logical reasoning. However, these methods encounter challenges with complex planning tasks, primarily due to extended reasoning steps, diverse constraints, and the challenge of handling multiple distinct sub-tasks. To address these challenges, we propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning. The hypertree structure enables LLMs to engage in hierarchical thinking by flexibly employing the divide-and-conquer strategy, effectively breaking down intricate reasoning steps, accommodating diverse constraints, and managing multiple distinct sub-tasks in a well-organized manner. We further introduce an autonomous planning framework that completes the planning process by iteratively refining and expanding the hypertree-structured planning outlines. Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6 times performance improvement over o1-preview.

Problem

• Addresses complex planning challenges in LLMs
• Enhances reasoning via hierarchical hypertree structures
• Improves performance in multi-subtask constraint handling

Innovation

• Hypertree-structured planning outlines for complex tasks
• Hierarchical thinking with divide-and-conquer strategy
• Autonomous iterative refinement of planning outlines