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:2607. 26066v1 Announce Type: cross Abstract: The growing volume of scientific submissions has motivated interest in using large language models (LLMs) to assist peer review.
By Ranjitha Shivaprasad Ballakuraya, Arash Mahyari, Ashok Srinivasan
arXiv:2606. 28277v1 Announce Type: cross Abstract: Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving.
By Rajesh Jayaram, Drew Tyler, David Woodruff, Corinna Cortes, Yossi Matias, Vahab Mirrokni, Vincent Cohen-Addad
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
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart.
arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.
By Yifan Zhang, Rahmatollah Beheshti
AtomCite is an agentic framework that verifies and corrects page‑level citations in multi‑page documents by parsing answers into claims, checking each claim against the cited page image, and applying a deterministic repair policy. The authors introduce DocCite, the first benchmark for this task, built on MP‑DocVQA and DUDE, containing 928 injected instances and 1,909 verified natural errors. Across Gemini, Claude, and GPT models, AtomCite achieves about 93% verification accuracy and improves citation precision from 34% to 87‑90%, while also enhancing hallucination detection in open‑source models.
By Chen Qian, Yimeng Wang, Yu Chen, Lingfei Wu, Andreas Stathopoulos
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
Large language models (LLMs) increasingly support complex professional tasks, yet their capabilities in rule-intensive document review remain insufficiently evaluated. National standard documents, such as China GB/T standards, offer a representative testbed: they are lengthy, highly structured, and governed by explicit rules for scope, terminology, normative wording, and cross-section consistency.
arXiv:2609.05947v1 Announce Type: new
Abstract: Peer review is central to quality control in science. However, existing evaluations of AI-assisted peer review mainly focus on the overall quality of g...
By Siming Yuan, Xueyi Zhang, Wangze Ni, Tianfang Xiao, Shimin Di, Jia Zhu, Zhuoren Jiang, Rong Tan, Lei Chen, Kui Ren
The paper "Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papers" introduces SciSlopBench, a dataset of 390 AI‑generated papers paired with human‑written counterparts, and defines six measures across Structure, Argument, and Artifacts to detect scientific slop. The authors show that these measures can identify AI papers with 85.9% accuracy and that higher slop correlates with lower ICLR ratings and distinguishes rejected from accepted papers. They also propose SciSlopHarness, a framework that guides a fixed LLM to revise only evidence‑supported sections, reducing the AI‑human gap by 63% without human reference targets.
By Yerim Oh, Young-Jun Lee, Jaewoo Ahn, Gunhee Kim, Dongyeop Kang
NovGauge is a new benchmark designed to diagnose large language models’ ability to assess scientific paper novelty. It contains 619 paper pairs and 50 multi-paper sets, each labeled along three dimensions—task, problem, and method—by experts from ICLR reviewer overlap claims and survey co-citations. The study evaluates 18 LLMs, revealing high hallucination rates and weak evidence grounding, with the best model achieving only 43‑72% verified F1 across dimensions.
By Guoqiang Zhang, Kexin Tan, Ming Zhang, Li Ju, Wenqing Jing, Zhonghan Yue, Jiayi Chen, Shiqiang Wu, Shaofan Liu, Yue Zhang, Yuankai Ying, Yang Shi, Tao Gui, Qi Zhang, Xuanjing Huang