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The paper introduces Execute-Summarize (ES), a framework that decouples task execution from workflow construction in LLMs, addressing the challenge of accurately translating LLM reasoning into structured workflows. ES first completes the task using available tools and then independently reconstructs a structured workflow from execution traces. Experiments on the newly introduced FlowBench demonstrate that ES outperforms existing methods, establishing a more reliable paradigm for grounding free-form LLM reasoning into structured workflows.
Decoupling execution from workflow generation in LLMs yields significantly more accurate and robust structured workflows.
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.