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

Computer Science > Artificial Intelligence arXiv:2604.23194 (cs) [Submitted on 25 Apr 2026] Title:From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents Authors:Haoran Tan, Zeyu Zhang, Chen Ma, Tianze Liu, Quanyu Dai, Xu Chen View a PDF of the paper titled From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents, by Haoran Tan and 4 other authors View PDF HTML (experimental) 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 this https URL. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.23194 [cs.AI] (or arXiv:2604.23194v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.23194 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Haoran Tan [view email] [v1] Sat, 25 Apr 2026 07:54:23 UTC (838 KB) Full-text links: Access Paper: View a PDF of the paper titled From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents, by Haoran Tan and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to…

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