The paper introduces an author-facing large language model (LLM) system that generates a broad set of atomic concerns about a manuscript and compresses them into a concise report, aiming to provide early peer‑review feedback. Evaluations on 3,398 ICLR 2026 submissions show that the system covers 44.9% of historical reviewer issues on a diagnostic set, rising to 78.7% strict coverage and 84.9% seriousness‑weighted coverage after deduplication and refill, using 3.6× more requests and 5.2× more tokens. Ablation studies reveal that representative selection and matcher sensitivity are key factors limiting the compression quality.
By Pouya Parsa, Amin Rezaei
arXiv:2603. 20450v2 Announce Type: replace-cross Abstract: A number of scientific conferences and journals have recently enacted policies that prohibit LLM usage by peer reviewers, except for polishing, paraphrasing, and grammar correction of otherwise human-written reviews.
By Rounak Saha, Gurusha Juneja, Dayita Chaudhuri, Naveeja Sajeevan, Nihar B Shah, Danish Pruthi
arXiv:2608. 10715v1 Announce Type: cross Abstract: Over the past several years, LLM-powered chatbots and agents have become widely used as a tool for academic writing.
By Lena Holzwarth, Rita Gonz\'alez-M\'arquez, Dmitry Kobak
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
The paper introduces Self‑Conditioning, an unsupervised, information‑theoretic estimator that measures the amount of external information in peer reviews. It compares the likelihood of a review under its original production context with the likelihood when that context is augmented by hints extracted from the review itself. On the IntelLabs benchmark, Self‑Conditioning can perfectly distinguish fully‑delegated reviews from machine‑polished ones, remains largely insensitive to surface rewriting, and shows that increased external input drives scores toward human‑like values, unlike standard ATD baselines.
By Matthieu Dubois, Pablo Piantanida, Fran\c{c}ois Yvon
arXiv:2607. 18360v1 Announce Type: cross Abstract: Large language models (LLMs) now routinely draft literature reviews and assist with academic writing, which means a higher risk of fabricated references: GPTZero found 53 papers with hallucinated citations among NeurIPS 2025's accepted set.
By Patrik Reizinger, Wieland Brendel
arXiv:2607. 19300v1 Announce Type: new Abstract: As LLM adoption becomes more widespread, there is a growing interest in detecting LLM-generated content, for example through LLM detection tools and through heuristics based on language patterns.
By Meena Jagadeesan, Tatsunori Hashimoto, Jon Kleinberg
arXiv:2604. 04074v4 Announce Type: replace Abstract: Large language model (LLM)-based reviewing systems typically assess manuscripts in isolation, leaving literature- and code-dependent claims difficult to verify.
By Ling Yue, Chaoqian Ouyang, Hang Xu, Ruijun Huang, Yuchen Liu, Libin Zheng, Wei Liu, Shaowu Pan, Shimin Di, Min-Ling Zhang
arXiv:2510. 02027v2 Announce Type: replace Abstract: Scholarly publishing requires scalable scrutiny supported by auditable evidence.
By Khalid M. Saqr
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
The paper investigates language-of-study (LoS) bias in NLP peer reviews, defining and distinguishing negative and positive forms of bias. Using a new dataset, LOBSTER, and an LLM-based detection pipeline, the authors analyze 15,645 reviews and find that non‑English papers experience significantly higher bias rates, with negative bias outweighing positive bias. They further identify four subcategories of negative bias, noting that demanding unjustified cross‑lingual generalization is the most common.
By Ehsan Barkhordar, Abdulfattah Safa, Verena Blaschke, Erika Lombart, Marie-Catherine de Marneffe, G\"ozde G\"ul \c{S}ahin