Wrong With Conviction

Why Confident Errors Evade Detection in Language Models · Nicholas Kasdaglis, Ph.D. · TOPP Interactive Design

Language models produce some wrong answers with the same internal and output signature as correct ones — confident errors. This work shows that unsupervised endpoint detectors, and semantic entropy, are structurally blind to confident errors, and that a supervised read of the model's internal state can detect the error regime, route a matching correction, and refuse when residual risk remains.