@inproceedings{li-etal-2025-understanding,
title = "Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld{'}s Episode Theory",
author = "Li, Ming and
Zhang, Nan and
Fan, Chenrui and
Jiao, Hong and
Fu, Yanbin and
Peters, Sydney and
Xu, Qingshu and
Lissitz, Robert and
Zhou, Tianyi",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.922/",
doi = "10.18653/v1/2025.emnlp-main.922",
pages = "18267--18288",
ISBN = "979-8-89176-332-6",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory
%A Li, Ming
%A Zhang, Nan
%A Fan, Chenrui
%A Jiao, Hong
%A Fu, Yanbin
%A Peters, Sydney
%A Xu, Qingshu
%A Lissitz, Robert
%A Zhou, Tianyi
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F li-etal-2025-understanding
%X 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.
%R 10.18653/v1/2025.emnlp-main.922
%U https://aclanthology.org/2025.emnlp-main.922/
%U https://doi.org/10.18653/v1/2025.emnlp-main.922
%P 18267-18288
Markdown (Informal)
[Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory](https://aclanthology.org/2025.emnlp-main.922/) (Li et al., EMNLP 2025)
ACL
- Ming Li, Nan Zhang, Chenrui Fan, Hong Jiao, Yanbin Fu, Sydney Peters, Qingshu Xu, Robert Lissitz, and Tianyi Zhou. 2025. Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 18267–18288, Suzhou, China. Association for Computational Linguistics.