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arxiv: 2604.23194 · v1 · submitted 2026-04-25 · 💻 cs.AI

Recognition: unknown

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

Haoran Tan , Zeyu Zhang , Chen Ma , Tianze Liu , Quanyu Dai , Xu Chen
Authors on Pith no claims yet

Pith reviewed 2026-05-08 08:05 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM agentshierarchical planningself-adaptive planningprogressive refinementtask executionimitation learningmulti-step tasksdecision making
0
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The pith

LLM agents improve multi-step task success by starting with coarse plans and refining detail only as task complexity requires.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces AdaPlan-H, a planning system for large language model agents that begins with a high-level overview and adds specificity step by step according to the demands of each task. This draws on the idea of progressive refinement to avoid the mismatch of fixed-detail planners, which either overload simple tasks or leave complex ones underspecified. The approach optimizes the resulting plans through imitation learning and capability enhancement. A reader would care because current agent planners lack flexibility across task difficulties, often leading to inefficiency or failure in dynamic environments. If the claim holds, agents could handle a wider range of real-world sequences more reliably without manual adjustment of planning depth.

Core claim

AdaPlan-H initiates with a coarse-grained macro plan and progressively refines it based on task complexity, generating self-adaptive hierarchical plans tailored to varying difficulty levels that are optimized by imitation learning and capability enhancement, leading to higher task execution success rates and reduced overplanning.

What carries the argument

Self-adaptive hierarchical planning mechanism that starts with a coarse macro plan and refines progressively to match task complexity.

If this is right

  • Task execution success rates rise for multi-step decision problems.
  • Overplanning is reduced at the planning stage across varying task difficulties.
  • The method supplies a flexible solution adaptable to both simple and complex tasks.
  • Plans can be further improved through imitation learning and capability enhancement.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same coarse-to-fine adaptation could be tested in non-LLM agent systems to check broader applicability.
  • Resource consumption during planning might drop when simple subtasks avoid unnecessary detail.
  • Integration with automatic complexity estimation could remove the need for external signals to trigger refinement.
  • Future agent designs might embed similar progressive mechanisms to scale better with environment size.

Load-bearing premise

That a progressive refinement strategy from cognitive science can be translated into an effective, learnable planning procedure for large language model agents.

What would settle it

A controlled comparison on standard benchmarks where AdaPlan-H produces no measurable gain in task completion rates or no reduction in overplanning compared to fixed-granularity baselines.

Figures

Figures reproduced from arXiv: 2604.23194 by Chen Ma, Haoran Tan, Quanyu Dai, Tianze Liu, Xu Chen, Zeyu Zhang.

Figure 1
Figure 1. Figure 1: The left part illustrates how hierarchical plans at different levels assist the agent in interacting with the view at source ↗
Figure 2
Figure 2. Figure 2: The overall architecture of AdaPlan-H with two-stage optimization. First of all, we construct the optimal view at source ↗
Figure 3
Figure 3. Figure 3: The distribution of the optimal number of levels for the hierarchical plans corresponding to each task in the training sets of ALFWorld. plan_1 55.0% plan_2 18.5% plan_3 26.5% Plan Level Distribution in ScienceWorld Train Set view at source ↗
Figure 4
Figure 4. Figure 4: plan_1 25.5% plan_2 46.4% plan_3 28.1% Plan Level Distribution in AlfWorld Train Set view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 0 minor

Summary. The paper proposes AdaPlan-H, a self-adaptive hierarchical planning mechanism for LLM agents. Drawing from progressive refinement in cognitive science, the method starts with a coarse-grained macro plan and progressively refines it according to detected task complexity, generating plans tailored to varying difficulty levels. The framework is optimized via imitation learning and capability enhancement. The authors report that experiments on standard agent benchmarks for multi-step complex decision-making tasks demonstrate significant gains in task execution success rates together with reduced overplanning.

Significance. If the experimental results hold, the work addresses a practical limitation of fixed-granularity planning in LLM agents by enabling adaptive detail levels, which could improve both efficiency and reliability on dynamic tasks. The commitment to releasing code and data publicly supports reproducibility and community follow-up.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our work and the recommendation to accept the paper. We appreciate that the significance of the adaptive hierarchical planning approach was recognized, particularly its potential to address fixed-granularity limitations in LLM agents.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper proposes a new method (AdaPlan-H) for self-adaptive hierarchical planning in LLM agents, drawing external inspiration from cognitive science on progressive refinement. It describes the coarse-to-fine mechanism, its implementation via imitation learning and capability enhancement, and validates improvements through experiments on standard benchmarks. No equations, self-definitions, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text or abstract. The central claims rest on the described architecture and empirical results rather than reducing to inputs by construction, rendering the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the applicability of cognitive science principles to LLM planning and the effectiveness of imitation learning for optimization, with no explicit free parameters or invented entities detailed in the abstract.

axioms (1)
  • domain assumption Progressive refinement from cognitive science applies directly to LLM agent planning and can be mimicked via self-adaptive hierarchies
    Invoked as the core inspiration without further justification in the abstract.

pith-pipeline@v0.9.0 · 5509 in / 1132 out tokens · 53722 ms · 2026-05-08T08:05:11.666111+00:00 · methodology

discussion (0)

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Reference graph

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  37. [37]

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