Held-out uncertainty and repeated noise

Repeated noise on $n$ fixed inputs can reduce Monte Carlo uncertainty conditional on those inputs. It does not turn them into $nR$ independent population examples.

The theorem permits selecting among the $J$ candidates after their simultaneous evaluation. It does not permit arbitrary additional test-driven retraining without expanding or changing the statistical analysis. Across-seed uncertainty requires independent training replicates and a seed-level analysis.