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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.

Last week, General Motors Co. announced plans to release a fleet of new electric vehicles by 2023, laying the groundwork for an "all-electric future." On Monday, the automotive giant revealed that it has bought Strobe, a Pasadena startup that produces the laser-based imaging technology known as lidar. Lidar uses a pulsed laser sensor to measure the distance between objects and is a crucial component of autonomous vehicles' navigation systems. But the technology's high price, complexity and limited performance has kept self-driving cars from being deployed on a larger scale, according to Kyle Vogt, chief executive of Cruise Automation, a subsidiary developing self-driving technology that GM bought last year. "To solve these problems we've acquired Strobe, a company that has quietly been building the leading next-generation lidar sensors," Vogt wrote in a blog post announcing the purchase of Strobe.