AI-Likeness in the Peer-Review Ecology: Evaluation-Response Asymmetry

AI-like language has been linked to favorable peer-review evaluations, but whether that association extends to the author–reviewer exchange remains unclear. We analyze 150,635 ICLR reviews from 2024–2026, linking reviews to author responses and reviewer follow-up. Using EditLens and CoCoDet as complementary continuous proxies, we benchmark their separation of matched human and directly generated reviews and compare downstream associations with earlier ICLR cohorts. Across both measurements, higher review-side AI-likeness is associated with higher ratings but less subsequent positive reviewer updating. In 2025–2026, its relation to author participation depends on the measurement: EditLens is associated with less rebuttal submission, whereas CoCoDet is associated with more. Pooled analyses generally link higher AI-likeness scores to more visible response organization, while within-paper comparisons attenuate or reverse the response-side association, suggesting that part of the pooled pattern reflects differences among papers. We call this pattern evaluation–response asymmetry: favorable evaluation does not extend uniformly to participation, organization, and reconsideration. These findings show that ratings alone do not characterize how AI-like language relates to peer review as an exchange.