Quantifying Faithful Confidence Expression in Large Reasoning Models

Framework for quantifying faithful confidence expression in large reasoning models

A new arXiv preprint introduces a framework to measure faithful calibration (FC) in large reasoning models (LRMs)—the alignment between a model's intrinsic confidence and its linguistically expressed confidence. The authors analyze linguistic decisiveness against three internal uncertainty sources (token probabilities, hidden states, sampled response consistency) and introduce prefix-conditioned sampling to handle structural variation in chain-of-thought traces. Applying the framework across leading models, they find FC is a significant and distinct failure mode for LRMs: extended reasoning traces do not automatically improve calibration, prompt interventions that help non-reasoning models fail in the reasoning setting, and different confidence estimators produce divergent assessments of the same traces.