arXiv:2605.29815v1 Announce Type: new Abstract: The growing number of submitted papers has motivated the exploration of Large Language Models (LLMs) as a means to support and augment the peer review process, particularly in terms of improving its speed and scalability. Yet, it remains unknown whether LLMs engage with scientific manuscripts in the same manner as human reviewers, or whether they merely produce review-looking text. To address this, we introduce the Peer Review AI Benchmark (PRAIB), a novel framework comprising thoroughly defined metrics that measure review specificity, style, and
The rapid adoption of LLMs and the increasing burden on academic peer review systems necessitates a robust framework for evaluating AI's role in this critical process.
This benchmark provides critical insight into the efficacy and potential pitfalls of integrating AI into scientific peer review, impacting research quality and publishing standards.
The introduction of PRAIB shifts the discourse from theoretical discussions about LLM utility in peer review to empirical evaluation, potentially accelerating or halting their widespread adoption.
- · Academic publishers
- · Researchers developing LLMs for scientific applications
- · AI ethics and validation researchers
- · LLM developers without robust scientific validation frameworks
- · Journals and conferences adopting LLM assistance without proper oversight
Researchers gain a standardized tool to assess how effectively LLMs can contribute to peer review.
The quality and speed of academic publishing could be significantly altered depending on PRAIB's findings and adoption.
The definition of intellectual contribution and human oversight in scientific validation may need to be re-evaluated globally.
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Read at arXiv cs.AI