Revisiting the Uniform Information Density Hypothesis in LLM Reasoning

The Uniform Information Density (UID) hypothesis proposes that effective communication is achieved by maintaining a stable flow of information.In this work, we revisit this principle in the context of Large Language Model (LLM) reasoning, asking whether step-level uniformity reflects reasoning quality.To this end, we introduce a novel framework to quantify uniformity of information flow at both local and global levels, using an entropy-based stepwise density metric.Across experiments on seven reasoning benchmarks, we see a counterintuitive pattern: while high-quality reasoning exhibit smooth step-by-step transitions (local uniformity) and structured, non-uniform information flow at the trajectory level (global nonuniformity).The results demonstrate that these uniformities outperform alternative internal signals as predictors of reasoning quality, and such divergence with human communication is not a model deficiency, but a byproduct of distinct objectives between human communication and LLM reasoning.