Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future
A comprehensive survey maps the emerging landscape of LLM-assisted peer review, cataloging techniques across review generation, rebuttal automation, and meta-review synthesis. The work synthesizes fine-tuning, agent-based, and reinforcement learning approaches while surfacing evaluation gaps and ethical tensions. For research infrastructure and publishing platforms, this represents a critical inflection point: as LLMs become viable reviewers, the field must reconcile quality assurance, bias mitigation, and reviewer accountability before deployment at scale.
Modelwire context
ExplainerSurvey papers tend to get read as neutral inventories, but this one surfaces something pointed: the evaluation frameworks for judging AI-generated reviews are themselves underdeveloped, meaning the field is building deployment pipelines before it has reliable ways to measure whether those pipelines produce good science.
The accountability gap here connects directly to what we covered in the PLOS and DataSeer piece on LLM-based research data reuse measurement. That work showed generative AI can track downstream scientific impact at scale, but it also illustrated how hard it is to validate what the model is actually counting. If measurement of data reuse is already contested, measurement of review quality is a harder problem by an order of magnitude. Separately, the 'Models Recall What They Violate' constraint-drift findings are directly relevant: a reviewer that can accurately restate a paper's claims while systematically drifting from evaluation criteria is not a reliable reviewer, and that failure mode is not hypothetical.
Watch whether any major journal publisher (Nature Portfolio, PLOS, or Elsevier) announces a formal pilot program with defined quality metrics within the next 12 months. A pilot with published evaluation criteria would signal the field has moved past the survey stage; silence would confirm the accountability gap this paper identifies is still blocking real deployment.
Coverage we drew on
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsLarge Language Models · LLMs · Peer Review Systems
Modelwire Editorial
This synthesis and analysis was prepared by the Modelwire editorial team. We use advanced language models to read, ground, and connect the day’s most significant AI developments, providing original strategic context that helps practitioners and leaders stay ahead of the frontier.
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