Quantifying and Understanding Uncertainty in Large Reasoning Models

This research addresses the critical challenge of quantifying uncertainty in Large Reasoning Models (LRMs), noting the limitations of traditional and existing Conformal Prediction (CP) methods. It aims to develop a statistically rigorous approach that considers logical connections, interprets uncertainty origins, and disentangles reasoning quality from answer correctness.