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This paper shows that majority-vote self-consistency backfires on a majority of hard science problems in GPQA Diamond for small instruction-tuned LLMs, and that verifier-free gates like plurality agreement or token entropy cannot prevent this. Pre-registered confirmatory experiments quantify the accuracy drop and suggest confidence does not track correctness.