Can We Believe AI Peer Review?

LLMs increasingly assist scientific peer review. This creates a feedback loop when generated reviews enter the training data of future LLMs. While recursive training has been studied broadly, its effects on scientific judgments remain poorly understood. We empirically study this effect in a controlled setting by varying synthetic-review exposure while fixing other training conditions. After one recursive step, increasing exposure from $0\%$ to $100\%$ makes recommendation ratings more concentrated, independently generated reviews of the same paper about $11\%$ more similar, and reviews across different papers about $5\%$ less diverse. A further step of recursive training amplifies homogenization when synthetic supervision is reintroduced. We characterize this task-specific phenomenon as $\textbf{scientific-judgment collapse}$. We also explore a training-free method using contrastive activation steering with a limited reference reviews. Controlled experiments show a partial recovery in recommendation agreement and judgment diversity across different papers, but not among independently generated reviews of the same paper. To explore this approach beyond controlled experiments, we develop $\textbf{TrustReviewer}$. We train the model on a large-scale curated data and apply activation steering at inference time. On held-out papers, steering increases exact recommendation match from $73.85\%$ to $75.40\%$, compared with $73.10\%$ for OpenReviewer on the same LLM backbone, while maintaining relatively diverse scientific judgments. Our findings quantify judgment homogenization under recursive training and motivate further research on preserving judgment diversity.

Large language model (LLM) systems are increasingly proposed to assist peer review, yet most evaluations judge the prose of machine-generated review text, not the validity of the numeric score a system assigns. We validate AIPR, which reads a submitted manuscript and emits five 0-100 quality dimensions and a weighted overall score, against the public decision outcomes of a major machine learning venue. AIPR grades by prompting alone, with no fine-tuning on reviews or decisions. Across 300 ICLR submissions with public decision tiers and reviewer ratings, graded under a frozen pipeline with hypotheses pre-registered before any score met any outcome, the overall score separates rejected from accepted submissions (AUROC 0.82, 95% CI 0.78-0.87), rises monotonically across tiers, and tracks the mean reviewer rating. The signal is strongest where we claim it: the lowest-scoring fifth is rejected far above the base rate, with oral papers absent. The validity comes mostly from the model: a one-paragraph prompt on the same model discriminates almost as well as the full pipeline (the small gap favours the pipeline but does not meet the pre-declared criterion, p = 0.09). What the engineering adds is reliability and a grounded review: AIPR's score barely moves across repeated runs (0.7 vs. 2.8 points within-paper SD) where the bare prompt swings, and the same pass returns a rubric-structured, evidence-grounded review rather than a bare number, with the human keeping the decision.

AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are. We address these questions by first surveying reviewer-facing AI policies across 111 leading AI/NLP conferences and medical journals, revealing substantial regulation differences between the two communities. Second, we evaluate AI-generated peer reviews at ICLR 2026 and Nature Communications using a novel dataset comprising original manuscript submissions and several hundred human- and machine-generated reviews. We compare reviews produced by open-source and proprietary models using complementary evaluation metrics, including LLM-as-a-Judge, score alignment, granularity, and overlap with human reviewers' concerns. Our results show that current LLMs can generate detailed and fluent reviews but exhibit systematic weaknesses, such as overly positive recommendations, generic criticism, and uneven evidence grounding. We demonstrate that aggregate quality scores alone can overestimate review quality and argue for multi-dimensional evaluation of AI-generated peer reviews.