Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-quality reviews, we define misinformed review points as either "weaknesses" in a review that contain incorrect premises, or "questions" in a review that can be already answered by the paper. We verify that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed. To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness. We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation. Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs. The models show F1 scores of 0.4–0.5 and kappa scores of 0.3–0.4, indicating moderate agreement but also suggesting that fully automating the evaluation remains challenging. A thorough disagreement analysis reveals that most errors are due to models’ incorrect reasoning. We also prove that evaluating premise-level factuality shows significantly higher agreements than evaluating weakness-level factuality.

This position paper presents a novel perspective on the utilization of Large Language Models (LLMs) in the artificial intelligence paper review process. We first critique the current tendency for LLMs to be primarily used for simple review text generation, arguing instead that this approach overlooks more meaningful applications of LLMs that preserve human expertise at the core of evaluation. Instead, we advocate for leveraging LLMs to support key aspects of the review process—specifically, verifying the reproducibility of experimental results, checking the correctness and relevance of citations, and assisting with ethics review flagging. For example, integrating tools based on LLM Agents for code generation from research papers has recently enabled automated assessment of the reproducibility of the paper, thereby improving the transparency and reliability of research. By reorienting LLM usage toward these targeted and assistive roles, we outline a pathway for more effective and responsible integration of LLMs into peer review, ultimately supporting both reviewer efficiency and the integrity of the scientific process.