构建可解析复杂推理过程的统一结构框架。
ReasoningFlow: Semantic Structure of Complex Reasoning Traces
- 将推理过程转为有向无环图，提取子图模式。
- 揭示大模型推理中的规划、反思等行为规律。
- 帮助理解与改进大模型的逻辑推理能力。
大型推理模型（LRMs）生成包含规划、反思、验证和回溯的复杂推理轨迹。本文提出ReasoningFlow，一种统一的分析框架，用于解析这些复杂轨迹的语义结构。该框架将推理轨迹解析为有向无环图，使不同推理模式可作为子图结构进行表征。这种人类可读的表示在理解、评估和提升LRMs的推理过程方面具有广泛应用前景。
原文摘要 · Abstract (English)
Large reasoning models (LRMs) generate complex reasoning traces with planning, reflection, verification, and backtracking. In this work, we introduce ReasoningFlow, a unified schema for analyzing the semantic structures of these complex traces. ReasoningFlow parses traces into directed acyclic graphs, enabling the characterization of distinct reasoning patterns as subgraph structures. This human-interpretable representation offers promising applications in understanding, evaluating, and enhancing the reasoning processes of LRMs.
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