Your thoughts tell who you are: Characterize the reasoning patterns of LRMs

Current comparisons of large reasoning models (LRMs) focus on macro-level statistics such as task accuracy or reasoning length. Whether different LRMs reason differently remains an open question. To address this gap, we introduce the LLM-proposed Open Taxonomy (LOT), a classification method that uses a generative language model to compare reasoning traces from two LRMs and articulate their distinctive features in words. LOT then models how these features predict the source LRM of a reasoning trace based on their empirical distributions across LRM outputs. Iterating this process over a dataset of reasoning traces yields a human-readable taxonomy that characterizes how models think. We apply LOT to compare the reasoning of 12 open-source LRMs on tasks in math, science, and coding. LOT identifies systematic differences in their thoughts, achieving 80-100% accuracy in distinguishing reasoning traces from LRMs that differ in scale, base model family, or objective domain. Beyond classification, LOT's natural-language taxonomy provides qualitative explanations of how LRMs think differently. Finally, in a case study, we link the reasoning differences to performance: aligning the reasoning style of smaller Qwen3 models with that of the largest Qwen3 during test time improves their accuracy on GPQA by 3.3-5.7%. Following the success of GPT -o1 and DeepSeek-R1, a wave of large reasoning models (LRMs) has recently become available. These models differ in training recipes and report varying benchmark performance, but far less is known about whether they also reason differently from one another. In this paper, we pose a fundamental question: can LRMs be distinguished by their reasoning patterns, and if so, what are the key distinguishing traits? A growing body of work has begun probing the reasoning style of individual LRMs, yielding insights into how artificial thinkers "think". Marjanovi c et al. (2025), for example, finds that DeepSeek-R1's reasoning depth correlates with the human cognitive load when processing complex sentences. Bogdan et al. (2025) annotates functions of DeepSeek-R1-Distill-Qwen's reasoning steps, showing that plan generation and re-evaluation are critical for solving math problems. However, only a few studies attempt broader comparisons across LRMs and on multiple reasoning behaviors. Gandhi et al. (2025) compares the reasoning patterns of base large language models (LLMs) and their RL fine-tuned variants, finding that the habits of base models correlate with fine-tuning gains.