Large Language Models (LLMs) can produce surprisingly sophisticated estimates of their own uncertainty. However, it remains unclear to what extent this expressed confidence is tied to the reasoning, knowledge, or decision making of the model. To test this, we introduce $\textbf{RiskEval}$: a framework designed to evaluate whether models adjust their abstention policies in response to varying error penalties. Our evaluation of several frontier models reveals a critical dissociation: models are neither cost-aware when articulating their verbal confidence, nor strategically responsive when deciding whether to engage or abstain under high-penalty conditions. Even when extreme penalties render frequent abstention the mathematically optimal strategy, models almost never abstain, resulting in utility collapse. This indicates that calibrated verbal confidence scores may not be sufficient to create trustworthy and interpretable AI systems, as current models lack the strategic agency to convert uncertainty signals into optimal and risk-sensitive decisions.

翻译：大语言模型（LLMs）能够产生令人惊讶的、关于其自身不确定性的复杂估计。然而，这种表达出的置信度在多大程度上与模型的推理、知识或决策过程相关联，目前尚不清楚。为了检验这一点，我们引入了 $\textbf{RiskEval}$：一个旨在评估模型是否会根据不同的错误惩罚调整其弃权策略的框架。我们对多个前沿模型的评估揭示了一个关键的分离现象：模型在阐述其口头置信度时并不具备成本意识，在高惩罚条件下决定是参与还是弃权时也缺乏策略性响应。即使当极端惩罚使得频繁弃权成为数学上的最优策略时，模型也几乎从不弃权，导致效用崩溃。这表明，经过校准的口头置信度分数可能不足以创建值得信赖且可解释的人工智能系统，因为当前模型缺乏将不确定性信号转化为最优且风险敏感的决策的策略性主体能力。