mlsanityData to 7 Oct 2026

Revisiting the UID Hypothesis in LLM Reasoning Traces

arXiv 2510.13850 ↗First posted 11 Oct 2025cs.CL cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Large language models (LLMs) often solve problems using step-by-step Chain-of-Thought (CoT) reasoning, yet these intermediate steps are frequently unfaithful or hard to interpret. Inspired by the Uniform Information Density (UID) hypothesis in psycholinguistics -- which posits that humans communicate by maintaining a stable flow of information -- we introduce entropy-based metrics to analyze the information flow within reasoning traces. Surprisingly, across three challenging mathematical benchmarks, we find that successful reasoning in LLMs is globally non-uniform: correct solutions are characterized by uneven swings in information density, in stark contrast to human communication patterns. This result challenges assumptions about machine reasoning and suggests new directions for designing interpretable and adaptive reasoning models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 27 of its 28 references that are in the map. First posted on arXiv 11 Oct 2025.

Builds on

27 of 28 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Qwen3 Technical Report14 May
  2. 2023Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents13 Oct
  3. 2023Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners24 May
  4. 2023ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness21 Apr
  5. 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning15 Dec

Other references · 22

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  4. 2023Let's Verify Step by Stepcited by 3,598
  5. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  6. 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 533
  7. 2025The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexitycited by 298
  8. 2025TTRL: Test-Time Reinforcement Learningcited by 253
  9. 2024Chain-of-Thought Reasoning Without Promptingcited by 208
  10. 2025Learning to Reason without External Rewardscited by 199
  11. 2025Scalable Best-of-N Selection for Large Language Models via Self-Certaintycited by 182
  12. 2025The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoningcited by 160
10 more
  1. 2025Thought Anchors: Which LLM Reasoning Steps Matter?cited by 126
  2. 2025Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivizationcited by 124
  3. 2021Revisiting the Uniform Information Density Hypothesiscited by 63
  4. 2025Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lenscited by 42
  5. 2023Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributionscited by 32
  6. 2025One-shot Entropy Minimizationcited by 32
  7. 2024Forking Paths in Neural Text Generationcited by 30
  8. 2024Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discoursecited by 14
  9. 2025Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?cited by 11
  10. 2025Training-free LLM Verification via Recycling Few-shot Examplescited by 4

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  1. 2025Revisiting the Uniform Information Density Hypothesis in LLM Reasoning
  2. 2023Concise and Organized Perception Facilitates Reasoning in Large Language Models
  3. 2024Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
  4. 2026LLM Reasoning Is Latent, Not the Chain of Thought
  5. 2026UniCog: Uncovering Cognitive Abilities of LLMs through Latent Mind Space Analysis
  6. 2025The Universal Landscape of Human Reasoning
  7. 2024Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus
  8. 2026Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning