将大模型推理与工作流生成分离，提升任务流程的准确性。
FlowMind: Execute-Summarize for Structured Workflow Generation from LLM Reasoning
- 先执行任务再独立重构流程，避免过程干扰。
- 在新构建的FlowBench上准确率显著超越现有方法。
- 适合需要可靠结构化流程的自动化系统开发者。
大模型可通过推理和工具使用解决复杂任务，但将这些解决方案准确转化为结构化工作流仍具挑战。本文将工作流建模为工具调用序列，重新定义问题为设计一种既能完成任务又能可靠构建工作流的机制。以往在执行过程中构建工作流的方法常因双重过程干扰导致不准确。为此提出Execute-Summarize（ES）框架：模型首先利用可用工具完成任务，随后独立地从执行轨迹中重建结构化工作流。这种解耦提升了工作流的准确性和鲁棒性。我们构建了FlowBench基准，并通过大量实验表明，该方法优于现有方法，为将自由形式的LLM推理落地为结构化工作流提供了可靠范式。
原文摘要 · Abstract (English)
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.
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