Tracing Uncertainty in Language Model Reasoning

Language model (LM) reasoning, commonly described as Chain-of-Thought or test-time scaling, often improves benchmark performance, but the dynamics underlying this process remain poorly understood. We study these dynamics through the lens of uncertainty quantification by treating the reasoning traces, the intermediate token sequences generated by LMs, as evolving model states. We summarize each trace by an uncertainty trace profile: a small set of features describing the shape of the uncertainty signal over its trace, such as its slope and linearity. We find that across eight LMs from three different families evaluated on eight datasets on mathematical and logical reasoning, these profiles predict whether a trace yields a correct final answer with AUROC up to $0.984$, improving markedly on recent related work. We reach AUROC $0.801$ using only the first few hundred tokens of full traces, suggesting that errors can be detected early in the generation. A detailed comparison of correct and incorrect traces further reveals qualitatively distinct uncertainty profiles, with correct traces showing a steeper and less linear decline in uncertainty. Together, the results suggest that our method, grounded in decision-making under uncertainty, provides a principled lens for studying the generative process underlying underlying LM reasoning.