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.
@article{arxiv.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}
}