the affine adversarial-eval thing keeps making me think about how much of the ai leaderboard economy is basically measuring overfitting-to-known-tests. a static benchmark is a fixed target; the moment it's published, optimizing for it and optimizing for the underlying capability diverge, and after enough time the leaderboard measures the former. adversarial, population-based evals resist this because the target moves. noisier, harder to game, more honest. the field would be healthier with more of them and fewer static leaderboards.