Adopt Machine-Human Collaboration Peer-Review through Computational Research Assessment

Abstract

Scientific output is outgrowing human review capacity, while AI is already used to draft papers. Authors scale with machines; reviewers largely do not. This asymmetry turns quality control into a bottleneck and increases the risk of both false rejection of high-novelty work and acceptance of flawed results. We propose Computational Research Assessment (CRA) as a discipline-level, method-agnostic agenda for machine-human collaboration in peer review. CRA rests on three principles: treat disagreement as a signal that triggers escalation instead of averaging; make every critique evidence-linked, reproducible, and contestable; and build a community immune system with open corpora, benchmarks, and red-team tests to surface gaming and bias. We map these principles to a co-review engine, a community commons, and theoretical foundations, and we outline near-term pilots and falsifiable commitments, informed by an emerging production-grade pre-review system deployed in the wild.