提出并行分解规划框架，解决大模型规划中约束多、错误累积问题。
Decompose, Plan in Parallel, and Merge: A Novel Paradigm for Large Language Models based Planning with Multiple Constraints
- 将复杂任务按约束分解为子任务，分并行生成子计划
- 在旅行规划任务中准确率提升37.2%，优于现有方法
- 适合需多约束协同的复杂决策场景
尽管大型语言模型（LLMs）取得显著进展，基于LLM的智能体在规划任务上仍面临挑战。现有方法存在约束过重和误差逐级传播两大缺陷。为此，我们提出一种新型并行规划范式DPPM（Decompose, Plan in Parallel, and Merge），即根据约束将复杂任务分解为子任务，在并行生成各子任务计划后，融合为全局计划。此外，该方法引入验证与优化模块，实现错误修正与冲突化解。实验表明，在旅行规划任务中，DPPM显著优于现有方法，准确率提升37.2%。
原文摘要 · Abstract (English)
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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