Revisiting the UID Hypothesis in LLM Reasoning Traces

• URL: http://arxiv.org/abs/2510.13850v1
• Date: Sat, 11 Oct 2025 21:19:17 GMT
• Title: Revisiting the UID Hypothesis in LLM Reasoning Traces
• Authors: Minju Gwak, Guijin Son, Jaehyung Kim,
• Abstract summary: Large language models (LLMs) often solve problems using step-by-step Chain-of-Thought (CoT) reasoning.<n>We introduce entropy-based metrics to analyze the information flow within reasoning traces.<n>We find that successful reasoning in LLMs is globally non-uniform.
• Score: 10.833681318622467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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