mlsanityData to 14 Oct 2026

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

arXiv 2509.24147 ↗First posted 29 Sep 2025cs.CL cs.AI cs.LGcited by 4

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

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%.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 16 of its 32 references that are in the map. First posted on arXiv 29 Sep 2025.

Builds on

16 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters6 Aug

Other references · 15

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  3. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  4. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 653
  5. 2025Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRscited by 377
  6. 2024CRUXEval: A Benchmark for Code Reasoning, Understanding and Executioncited by 300
  7. 2025Phi-4-reasoning Technical Reportcited by 151
  8. 2025Thought Anchors: Which LLM Reasoning Steps Matter?cited by 129
  9. 2025DeepSeek-R1 Thoughtology: Let's think about LLM Reasoningcited by 92
  10. 2025AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learningcited by 87
  11. 2025Reinforcing General Reasoning without Verifierscited by 77
  12. 2025Magistralcited by 60
3 more
  1. 2025Seed-Coder: Let the Code Model Curate Data for Itselfcited by 49
  2. 2025What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoningcited by 21
  3. 2025The Few-shot Dilemma: Over-prompting Large Language Modelscited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Datacited by 8
  2. 2026On the Role of Reasoning Patterns in the Generalization Discrepancy of Long Chain-of-Thought Supervised Fine-Tuningcited by 4
  3. 2026Constraint-Rectified Training for Efficient Chain-of-Thoughtcited by 0
  4. 2026Reason in Style: Discovering and Controlling Style in Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Cognitive Profiling of LRMs' Reasoning Traces Using Bloom's Taxonomy
  2. 2025Probing the "Psyche'' of Large Reasoning Models: Understanding Through a Human Lens
  3. 2025A Study on Thinking Patterns of Large Reasoning Models in Code Generation
  4. 2026Reasoning Structure of Large Language Models
  5. 2025Thinking Isn't an Illusion: Overcoming the Limitations of Reasoning Models via Tool Augmentations
  6. 2024Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
  7. 2025THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models
  8. 2025The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity