From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents

2026-04-25

AI Summary

This work addresses the limitations of existing large language model (LLM) agents, which typically employ fixed-granularity planning mechanisms that struggle to balance efficiency on simple tasks with the detailed reasoning required for complex ones. To overcome this, the paper introduces AdaPlan-H, a cognitively inspired adaptive hierarchical planning framework that, for the first time, integrates a progressive refinement strategy into LLM agents to enable dynamic, task-difficulty-aware adjustment of planning granularity. By synergistically combining hierarchical task decomposition, imitation learning, and capability enhancement, AdaPlan-H supports the adaptive generation and continuous optimization of planning hierarchies. Experimental results demonstrate that AdaPlan-H significantly improves success rates on multi-step complex tasks while effectively avoiding over-planning, thereby validating its efficiency and flexibility.

Technology Category

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

Application Category

• 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
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ planning mechanisms to guide long-term actions in dynamic environments. However, current planning approaches face a fundamental limitation that they operate at a fixed granularity level. Specifically, they either provide excessive detail for simple tasks or insufficient detail for complex ones, failing to achieve an optimal balance between simplicity and complexity. Drawing inspiration from the principle of \textit{progressive refinement} in cognitive science, we propose \textbf{AdaPlan-H}, a self-adaptive hierarchical planning mechanism that mimics human planning strategies. Our method initiates with a coarse-grained macro plan and progressively refines it based on task complexity. It generates self-adaptive hierarchical plans tailored to the varying difficulty levels of different tasks, which can be optimized by imitation learning and capability enhancement. Experimental results demonstrate that our method significantly improves task execution success rates while mitigating overplanning at the planning level, providing a flexible and efficient solution for multi-step complex decision-making tasks. To contribute to the community, our code and data will be made publicly available at https://github.com/import-myself/AHP.

Problem

• hierarchical planning
• LLM agents
• adaptive granularity
• multi-step tasks
• planning efficiency

Innovation

• self-adaptive planning
• hierarchical planning
• progressive refinement
• LLM agents
• imitation learning