FlowMind: Execute-Summarize for Structured Workflow Generation from LLM Reasoning
AI 摘要
该论文提出了一种Execute-Summarize框架,用于从LLM推理中生成更准确的结构化工作流。
主要贡献
- 提出Execute-Summarize框架,解耦任务执行和工作流构建
- 引入FlowBench基准测试
- 实验证明该方法优于现有方法
方法论
首先使用LLM和工具完成任务,然后独立地从执行轨迹中重建结构化工作流,避免了执行过程中的干扰。
原文摘要
LLMs can solve complex tasks through reasoning and tool use, but accurately translating these solutions into structured workflows remains challenging. We model workflows as sequences of tool use and reformulate the problem as designing a mechanism that can both solve tasks and reliably construct workflows. Prior approaches that build workflows during execution often suffer from inaccuracies due to interference between the two processes. We propose an Execute-Summarize(ES) framework that decouples task execution from workflow construction: the model first completes the task using available tools, then independently reconstructs a structured workflow from execution traces. This separation improves workflow accuracy and robustness. We introduce FlowBench and show through extensive experiments that our approach outperforms existing methods, providing a reliable paradigm for grounding free-form LLM reasoning into structured workflows.