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

• URL: http://arxiv.org/abs/2604.23194v1
• Date: Sat, 25 Apr 2026 07:54:23 GMT
• Title: From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents
• Abstract summary: Large language model-based agents have emerged as powerful approaches for solving dynamic and multi-step tasks.<n>We propose textbfAdaPlan-H, a self-adaptive hierarchical planning mechanism that mimics human planning strategies.<n>Our method significantly improves task execution success rates while mitigating overplanning at the planning level.
• Score: 24.79854509473032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Planner Matters! An Efficient and Unbalanced Multi-agent Collaboration Framework for Long-horizon Planning [32.56971325545821]
  Language model (LM)-based agents have demonstrated promising capabilities in automating complex tasks from natural language instructions.<n>We propose an enhanced multi-agent framework that decomposes automation into three roles: a planner for high-level decision-making, an actor for task execution, and a memory manager for contextual reasoning.
  arXiv  Detail & Related papers  (2026-05-04T02:58:05Z)
• Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams [11.964765256346857]
  Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments.<n>We present Scale-Plan, a scalable LLM-assisted framework that generates compact, task-relevant problem representations from natural language instructions.<n>By filtering irrelevant information prior to planning, Scale-Plan enables efficient decomposition, allocation, and long-horizon plan generation.
  arXiv  Detail & Related papers  (2026-03-09T18:13:18Z)
• TodoEvolve: Learning to Architect Agent Planning Systems [68.48983335970901]
  TodoEvolve is a meta-planning paradigm that autonomously synthesizes and dynamically revises task-specific planning.<n>PlanFactory provides a common interface for heterogeneous planning patterns.<n>TodoEvolve consistently surpasses carefully engineered planning modules while maintaining economical API costs and runtime overhead.
  arXiv  Detail & Related papers  (2026-02-08T06:37:01Z)
• HiPlan: Hierarchical Planning for LLM-Based Agents with Adaptive Global-Local Guidance [11.621973074884002]
  HiPlan is a hierarchical planning framework for large language model (LLM)-based agents.<n>It decomposes complex tasks into milestone action guides for general direction and step-wise hints for detailed actions.<n>In the offline phase, we construct a milestone library from expert demonstrations, enabling structured experience reuse.<n>In the execution phase, trajectory segments from past milestones are dynamically adapted to generate step-wise hints.
  arXiv  Detail & Related papers  (2025-08-26T14:37:48Z)
• HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking [109.09735490692202]
  We propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning.<n> 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.
  arXiv  Detail & Related papers  (2025-05-05T02:38:58Z)
• Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks [36.63527489464188]
  Plan-and-Act is a framework that incorporates explicit planning into large language models (LLMs)<n>Plan-and-Act consists of a Planner model which generates structured, high-level plans to achieve user goals, and an Executor model that translates these plans into environment-specific actions.<n>We present a state-of-the-art 57.58% success rate on the WebArena-Lite benchmark as well as a text-only state-of-the-art 81.36% success rate on WebVoyager.
  arXiv  Detail & Related papers  (2025-03-12T17:40:52Z)
• Interactive and Expressive Code-Augmented Planning with Large Language Models [62.799579304821826]
  Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. We propose REPL-Plan, an LLM planning approach that is fully code-expressive and dynamic.
  arXiv  Detail & Related papers  (2024-11-21T04:23:17Z)
• Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning [94.76546523689113]
  We introduce CodePlan, a framework that generates and follows textcode-form plans -- pseudocode that outlines high-level, structured reasoning processes. CodePlan effectively captures the rich semantics and control flows inherent to sophisticated reasoning tasks. It achieves a 25.1% relative improvement compared with directly generating responses.
  arXiv  Detail & Related papers  (2024-09-19T04:13:58Z)
• AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation [81.32722475387364]
  Large Language Model-based agents have garnered significant attention and are becoming increasingly popular.<n>Planning ability is a crucial component of an LLM-based agent, which generally entails achieving a desired goal from an initial state.<n>Recent studies have demonstrated that utilizing expert-level trajectory for instruction-tuning LLMs effectively enhances their planning capabilities.
  arXiv  Detail & Related papers  (2024-08-01T17:59:46Z)
• AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
  Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
  arXiv  Detail & Related papers  (2023-05-26T05:52:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.