LRM的推理链常被用户视为深思熟虑的证据，但这项研究戳破了这个幻觉——推理行为并不等于置信度表达更可靠。做模型对齐或安全评估的团队值得关注，尤其是那些在医疗、金融等高风险场景部署LRM的开发者，看完会重新审视你的置信度校准策略。
该研究提出一个系统框架，用于量化大型推理模型（LRM）在输出长链思维时，其内在置信度与语言表达置信度之间的对齐程度（即忠实校准FC）。研究发现，LRM的推理行为并不会自动提升FC，且针对非推理模型的提示干预在推理场景中无效。不同置信度估计器对同一推理轨迹给出分歧评估，暴露了现有评估方法的脆弱性。这项工作将FC确立为LRM在高风险部署场景下的关键可靠性与对齐目标。
Quantifying Faithful Confidence Expression in Large Reasoning Models
Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic and (linguistically) expressed confidence--is a persistent failure mode. This challenge is key for large reasoning models (LRMs), whose extended reasoning traces are often interpreted by users as evidence of deliberation, competence, and confidence. Despite the importance of FC and wide usage of LRMs, the extent to which LRMs can faithfully express their confidence remains poorly understood. Moreover, the prevailing paradigm to measure FC does not generalize well to the long chain-of-thought outputs generated by LRMs, which tend to lack clear step boundaries, involve inconsistent step structure, and encode complex conditional dependencies throughout the trace--complicating estimation of intrinsic confidence. To address this challenge, we introduce a novel framework to systematically quantify FC of LRMs. Our framework analyzes linguistic decisiveness relative to three sources of internal uncertainty, based on token probabilities, hidden states, and sampled response consistency. We also devise a prefix-conditioned sampling approach to control for conditional and structural variation across traces. Applying our framework to a diverse suite of leading models, datasets, and prompts, we find that faithful confidence expression is a significant challenge for LRMs. Reasoning behaviors do not automatically translate to improved FC, and prompt interventions for non-reasoning models do not improve faithfulness in the reasoning setting. Different confidence estimators further produce divergent assessments of the same traces, revealing fragility in prior evaluation methodologies. Taken together, our work establishes FC as a distinct reliability and alignment target for LRMs, particularly as such systems are increasingly deployed in high-stakes contexts.