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EquiReview-R improves AI-assisted peer review by refining structured concerns with evidence to reduce omission and overcritique, enhancing review accuracy.
This paper introduces a cross-model map for certifying selective predictors that must meet both an automation floor and a risk ceiling under covariate shift, deriving feasibility frontiers and two-resource sample-complexity trade-offs.