PaperDoctor is an agent framework that provides evidence‑grounded, actionable feedback for scientific papers before submission. It evaluates writing, layout, references, code, theory, prior work, and experiments through a three‑layer hierarchical system, linking each critique to specific evidence and revision suggestions. The system selectively rebuilds and reruns experiments to uncover reproducibility gaps, and an interactive interface lets authors explore findings tied to their manuscript.
By Kevin Qinghong Lin, Siyuan Hu, Pan Lu, Yu Chen, Yanzhe Chen, Owen Queen, Yupeng Chen, Jialin Yu, Junchi Yu, Zifeng Ding, Yuanfeng Ji, Sheng Liu, Jindong Gu, Linjie Li, Mike Zheng Shou, Philip Torr, James Zou
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:2609.14738v1 Announce Type: new
Abstract: Automated reviewing systems are increasingly evaluated based on the quality of the reviews they produce. Yet a review is only useful if acting on it le...
By Vidushee Vats, Karun Sharma, Shengzhi Li, Shichao Pei
The paper presents an LLM-driven framework that splits peer reviews into argumentative segments, detects multiple co-occurring issues such as lazy thinking and lack of specificity, and generates targeted, guideline-aware feedback using issue-specific templates. An iterative, reranking-based generation algorithm refines the feedback, and a controlled rewriting study shows it can reduce guideline violations by up to 92.4%. The authors also release LazyReviewPlus, a multi-label dataset of 1,309 sentences annotated for detecting lazy thinking and lack of specificity.
By Sukannya Purkayastha, Qile Wan, Anne Lauscher, Lizhen Qu, Iryna Gurevych
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
Operational reporting for high-performance computing (HPC) facilities requires combining information from monitoring services, schedulers, incident and maintenance records, storage measurements, perio...
arXiv:2601. 14171v2 Announce Type: replace Abstract: Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details.
By Qianli Ma, Chang Guo, Zhiheng Tian, Siyu Wang, Jipeng Xiao, Yuanhao Yue, Zhipeng Zhang
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
The paper investigates how newer GPT models alter scientific text, a phenomenon termed defensive writing, where the models either retract or weaken authors’ claims. Experiments show that as GPT versions advance, defensive writing becomes more pronounced, especially when the model is prompted to anticipate reviewer concerns. The study finds that this effect is largely driven by the anticipated-review explanation rather than mere correction of overclaiming, and that while AI reviewers rate such rewrites favorably, human readers perceive them as harder to read and less confident.
By Junchi Liao
The paper introduces TRAILS, a taxonomy of 38 mechanisms that can compromise the reliability of large language model (LLM) read‑and‑evaluate tasks in evidence synthesis, such as study screening, data extraction, risk‑of‑bias assessment, and critical appraisal. It explains how these mechanisms can cause inconsistent or hidden failures across repeated LLM runs and outlines deployment settings, testing approaches, and mitigation strategies for each. The authors argue that repeatability or accuracy alone does not guarantee trustworthy judgments, emphasizing the need for human evaluation to verify that decisions are evidence‑based, free from external influence, and reproducible.
By Carole Lunny, Ian Goodman, Stefano Berrone, Connor TA Brenna, Emma K Reid, Ivan Perez-Neri, Shailesh Kolekar, Nityanand Jain, Ashwin Kamath, Lucas HCC Santos, Yulia Otmakhova, Heba Hussein, Diana M Cespedes, Gioia Dahdal, Salmaan Kanji
arXiv:2608. 08975v1 Announce Type: cross Abstract: As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices shape AI-review judgments when reported scientific content is preserved and how these effects vary across evaluation conditions.
By Ming Li, Chenguang Wang, Xirui Li, Xinyue Zeng, Dianqi Li, Peng Shi, Dawei Zhou, Tianyi Zhou
arXiv:2606. 06025v1 Announce Type: cross Abstract: Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback.
By Xinpeng Qiu, Wang Yihu, Zhifeng Liu, Xiaochen Wang, Jimin Wang