Understanding the Thinking Process of Reasoning Models: A Perspective
  from Schoenfeld's Episode Theory
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.
68d5f2608ccd91bdd39ffd55
Ming Li
admin_assigned
2025-09-26T16:22:06.704Z
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/avatars/9d8c94b6ab8ad8b4faba3221b7e76053.svg
Ming Li
user
MingLiiii
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Nan Zhang
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Chenrui Fan
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Hong Jiao
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Yanbin Fu
admin_assigned
2025-09-26T16:22:19.431Z
6851f9a83f5a57f68184d4e9
/avatars/625a034424dd8b30fad71e76134d6424.svg
Yanbin Fu
user
ybfu
68d5f2608ccd91bdd39ffd5a
Sydney Peters
68d5f2608ccd91bdd39ffd5b
Qingshu Xu
68d5f2608ccd91bdd39ffd5c
Robert Lissitz
68d5f2608ccd91bdd39ffd5d
Tianyi Zhou
Understanding the Thinking Process of Reasoning Models: A Perspective
  from Schoenfeld's Episode Theory
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.
A novel framework using Schoenfeld's Episode Theory is introduced to analyze the reasoning patterns of Large Reasoning Models in solving math problems, providing a benchmark for machine reasoning.
Large Reasoning Models Schoenfeld's Episode Theory cognitive framework reasoning traces annotation corpus annotation guidebooks cognitive states transition dynamics controllable reasoning systems transparent reasoning systems