Quantifying Faithful Confidence Expression in Large Reasoning Models

Faithful calibration, the alignment between a model's internal and expressed confidence, is a significant challenge in large reasoning models. When these models' extended reasoning traces are misinterpreted as evidence of deliberation, competence, and confidence, it can lead to misplaced trust. Researchers have been working to quantify faithful confidence expression in large reasoning models, a crucial aspect of reliable uncertainty communication. The lack of faithful calibration can have far-reaching implications, affecting not only the technology itself but also policy, security, and workforce dynamics¹. Large reasoning models are particularly vulnerable to this issue, as their complex reasoning processes can be difficult to interpret. Quantifying faithful confidence expression is essential to developing more trustworthy models. This matter is critical for practitioners, as it directly impacts the reliability and trustworthiness of large reasoning models, making it essential to address this challenge to ensure the safe and effective deployment of these models.