Large Language Models (LLMs) have demonstrated exceptional abilities in reasoning for task planning. However, challenges remain under-explored for parallel schedules. This paper introduces a novel paradigm, plan-over-graph, in which the model first decomposes a real-life textual task into executable subtasks and constructs an abstract task graph. The model then understands this task graph as input and generates a plan for parallel execution. To enhance the planning capability of complex, scalable graphs, we design an automated and controllable pipeline to generate synthetic graphs and propose a two-stage training scheme. Experimental results show that our plan-over-graph method significantly improves task performance on both API-based LLMs and trainable open-sourced LLMs. By normalizing complex tasks as graphs, our method naturally supports parallel execution, demonstrating global efficiency. The code and data are available at https://github.com/zsq259/Plan-over-Graph.

本研究解决了大语言模型在并行调度中的挑战，提出了一种新范式“图上计划”，首先将真实的文本任务分解为可执行的子任务，并构建抽象任务图。通过设计自动化和可控的管道生成合成图以及提出两阶段训练方案，实验结果表明该方法显著提升了任务性能，展现出全球效率。

图上计划：朝着可并行化的LLM代理调度