Understanding the Thinking Process of Reasoning Models: A Perspective
  from Schoenfeld's Episode Theory
Authors: name: Ming Li, name: Nan Zhang, name: Chenrui Fan, name: Hong Jiao, name: Yanbin Fu, name: Sydney Peters, name: Qingshu Xu, name: Robert Lissitz, name: Tianyi Zhou

While Large Reasoning Models (LRMs) generate extensive chain-of-thought
reasoning, we lack a principled framework for understanding how these thoughts
are structured. In this paper, we introduce a novel approach by applying
Schoenfeld's Episode Theory, a classic cognitive framework for human
mathematical problem-solving, to analyze the reasoning traces of LRMs. We
annotated thousands of sentences and paragraphs from model-generated solutions
to math problems using seven cognitive labels (e.g., Plan, Implement, Verify).
The result is the first publicly available benchmark for the fine-grained
analysis of machine reasoning, including a large annotated corpus and detailed
annotation guidebooks. Our preliminary analysis reveals distinct patterns in
LRM reasoning, such as the transition dynamics between cognitive states. This
framework provides a theoretically grounded methodology for interpreting LRM
cognition and enables future work on more controllable and transparent
reasoning systems.