ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces
Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluation and monitoring of the reasoning process. We introduce ReasoningFlow, a framework that captures the discourse structures of LRM reasoning traces into fine-grained directed acyclic graphs (DAGs). We develop and validate our annotation schema through careful manual annotation of 31 traces (2.1k steps), achieving high inter-annotator agreement, then scale to automatic annotation of 1,260 traces (247.7k steps) spanning three tasks (math, science, argumentation) and five models (Qwen2.5-32B-Inst, QwQ-32B, DeepSeek-V3, DeepSeek-R1, GPT-oss-12...
| Authors | Jinu Lee, Shivam Agarwal, Amruta Parulekar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 June 2026 |
| Last updated at source | 11 September 2026 |
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