FlowMind: Execute-Summarize for Structured Workflow Generation from LLM Reasoning
On the map: LLM reasoning → Reasoning for agents, planning and code
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 26 of its 34 references that are in the map. First posted on arXiv 12 Feb 2026.
Builds on
26 of 34 references.
Closest ancestors · 2
Same direction, newest first
- 2025Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation3 Jul
- 2024AFlow: Automating Agentic Workflow Generation14 Oct
Other references · 24
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- 2025A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibilitycited by 89
- 2024AutoFlow: Automated Workflow Generation for Large Language Model Agentscited by 76
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- 2024StateFlow: Enhancing LLM Task-Solving through State-Driven Workflowscited by 65
- 2024WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Modelscited by 28
- 2025DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AIcited by 22
12 more
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- 2025United Minds or Isolated Agents? Exploring Coordination of LLMs under Cognitive Load Theorycited by 10
- 2025Benchmarking Failures in Tool-Augmented Language Modelscited by 9
- 2025Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflowscited by 6
- 2023STEPS: A Benchmark for Order Reasoning in Sequential Taskscited by 5
- 2025Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoningcited by 5
- 2025AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Sciencecited by 4
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- 2025Opus: A Prompt Intention Framework for Complex Workflow Generationcited by 3
- 2025CogniLoad: A Synthetic Natural Language Reasoning Benchmark With Tunable Length, Intrinsic Difficulty, and Distractor Densitycited by 3
- 2025Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Datacited by 1
- 2025Classifier-Augmented Generation for Structured Workflow Predictioncited by 1
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