Synthetic reviews can make AI peer review converge on narrower judgments
Training AI reviewers on model-written reviews can narrow their ratings and reduce the range of ideas they use, putting future AI-assisted paper evaluation at risk of scientific-judgment collapse. In a controlled study, researchers trained successor reviewers on different mixtures of official and synthetic ICLR reviews, then introduced TrustReviewer, which uses curated training data and activation steering to preserve judgment diversity without new expert annotation at test time.