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.
arXiv:2608. 03581v1 Announce Type: cross Abstract: 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.
By Alexander M. Fichtl, Lukas Ellinger, Josefin Kelber, Kry\v{s}tof Ol\'ik, Georg Groh
arXiv:2506. 08134v4 Announce Type: replace Abstract: Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale.
By Qiyao Wei, Samuel Holt, Jing Yang, Markus Wulfmeier, Mihaela van der Schaar
The paper introduces a verification‑centric framework and benchmark for evaluating large language models (LLMs) as assistants in peer review, focusing on error detection rather than imitation of human reviews. It presents a scalable benchmark that injects synthetic errors into conference papers to test LLMs’ ability to spot logical contradictions, and proposes a Multi‑Layered Review (MLR) framework that emphasizes deep manuscript comprehension before generating reviews. Experiments show the approach aligns well with human review scores, excels at error detection, and offers complementary reviewer insights, while also revealing vulnerabilities to adversarial manipulation.
By Rachel S. Y. Teo, Yutaro Yamada, Shashank Kotyan, Yuki Imajuku, Tarin Clanuwat
arXiv:2609.23264v1 Announce Type: new
Abstract: Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high sc...
By Shakiba Amirshahi, Sajad Ebrahimi, Hai Son Le, Negar Arabzadeh, Ebrahim Bagheri
Large language models are increasingly used as automated reviewers in scientific evaluation, creating a recursive feedback loop where later reviewers learn from earlier model-generated judgments. A study using Llama 3.1 8B fine‑tuned on ICLR reviews shows that incorporating synthetic reviews compresses rating distributions and reduces semantic diversity, a phenomenon termed scientific‑judgment collapse. To counter this, the authors introduce TrustReviewer, an open‑source LLM system that curates training data and applies paired activation steering at test time to preserve judgment diversity and improve recommendation alignment.
By Sy-Tuyen Ho, Minghui Liu, Furong Huang
arXiv:2606. 19749v1 Announce Type: new Abstract: A new class of agentic review systems are emerging as a remedy to the pressure placed on peer review systems by AI-assisted research, but it is unclear how they should be evaluated.
By Dang Nguyen, Wanqing Hao, Yanai Elazar, Chenhao Tan
arXiv:2606.13349v2 Announce Type: replace
Abstract: Effective peer review requires adaptive investigation that responds to evidence emerging from each paper. Existing LLM-based reviewers largely orga...
By Haishuo Fang, Yue Feng, Iryna Gurevych
arXiv:2609.25046v1 Announce Type: new
Abstract: Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely...
By Alireza Daghighfarsoodeh, Sajad Ebrahimi, Ali Ghorbanpour, Soroush Sadeghian, Radin Cheraghi, Negar Arabzadeh, Ebrahim Bagheri
arXiv:2606. 10159v1 Announce Type: cross Abstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage.
By Lin Li, Qi Zhang, Xander Davies, Jianing Qiu, Yarin Gal
FMMD is a multimodal, multidisciplinary dataset of open peer reviews from F1000Research that pairs manuscript-level visual and structural data with version‑specific reviewer reports and editorial decisions. It addresses key gaps in existing datasets by preserving precise alignment between review comments and the exact manuscript version, and by including a wide range of scientific disciplines beyond computer science. The dataset supports tasks such as visual‑semantic consistency classification, figure‑related review comment generation, and editorial decision prediction, providing a comprehensive empirical resource for multimodal automated scholarly paper review research.
By Zhenzhen Zhuang, Yuqing Fu, Jing Zhu, Zhangping Zhou, Jialiang Lin
The paper investigates whether peer review quality is declining by developing a framework for evidence-based comparison across venues and time. It applies this framework to major AI and machine learning conferences—ICLR, NeurIPS, and ACL—documenting diverse review formats and proposing a multi-dimensional schema that combines LLM-based and lightweight metrics. The study finds no consistent decline in median review quality over time, offering alternative explanations and recommendations for future empirical research.
By Ilia Kuznetsov, Rohan Nayak, Alla Rozovskaya, Iryna Gurevych