This September 18 2025 paper introduces a research project that applies Schoenfeld’s Episode Theory, a classic cognitive framework for analyzing human mathematical problem-solving, to understand the reasoning processes of Large Reasoning Models (LRMs). The authors created a novel, publicly available benchmark by annotating thousands of sentences and paragraphs from model-generated solutions to math problems, using seven cognitive labels such as Plan, Implement, and Verify. This approach offers a theoretically grounded methodology for interpreting LRM cognition, demonstrating that machine reasoning exhibits structured, episodic patterns similar to human behavior. The resulting annotated corpus and analytical protocol aim to enable the development of more transparent and controllable reasoning systems. Source: https://arxiv.org/pdf/2509.14662