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Decompose, Plan in Parallel, and Merge: A Novel Paradigm for Large Language Models based Planning with Multiple Constraints

3 June 2025
Zhengdong Lu
Weikai Lu
Yiling Tao
Yun Dai
ZiXuan Chen
Huiping Zhuang
Cen Chen
Hao Peng
Ziqian Zeng
ArXiv (abs)PDFHTML
Main:8 Pages
7 Figures
Bibliography:2 Pages
4 Tables
Appendix:3 Pages
Abstract

Despite significant advances in Large Language Models (LLMs), planning tasks still present challenges for LLM-based agents. Existing planning methods face two key limitations: heavy constraints and cascading errors. To address these limitations, we propose a novel parallel planning paradigm, which Decomposes, Plans for subtasks in Parallel, and Merges subplans into a final plan (DPPM). Specifically, DPPM decomposes the complex task based on constraints into subtasks, generates the subplan for each subtask in parallel, and merges them into a global plan. In addition, our approach incorporates a verification and refinement module, enabling error correction and conflict resolution. Experimental results demonstrate that DPPM significantly outperforms existing methods in travel planning tasks.

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@article{lu2025_2506.02683,
  title={ Decompose, Plan in Parallel, and Merge: A Novel Paradigm for Large Language Models based Planning with Multiple Constraints },
  author={ Zhengdong Lu and Weikai Lu and Yiling Tao and Yun Dai and ZiXuan Chen and Huiping Zhuang and Cen Chen and Hao Peng and Ziqian Zeng },
  journal={arXiv preprint arXiv:2506.02683},
  year={ 2025 }
}
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