arXiv:2606. 28363v1 Announce Type: cross Abstract: Objective: To describe the architecture and design rationale of meta-pipe, an open-source large language model (LLM)-agent pipeline that integrates the complete systematic review and meta-analysis (SR/MA) workflow -- from literature search through statistical analysis, manuscript generation, and quality assurance -- with mandatory human oversight at critical decision points.
By Hsieh-Ting Lin, Jiunn-Tyng Yeh
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: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
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
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
The paper introduces EquiReview‑R, an AI‑assisted review system that treats omission and over‑critique as distinct risks and refines a structured concern set using evidence‑linked reasoning. It demonstrates that more criticism does not guarantee a better review, showing that many high‑recall reviews lack definitive evidence for concerns and that revision before further search is essential. On a held‑out set of papers, EquiReview‑R meets non‑inferiority for major omission, cuts major over‑critique from 15.5 % to 8.1 %, and stops on 52.4 % of papers, with gains attributed to revision rather than extra inference.
By Zexing Zhang, Jichao Li, Tianyang Lei, Yude Fu, Yang Kewei
arXiv:2608.21374v1 Announce Type: new
Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspec...
By Ruotong Zhao, Zhiyu Chen, Xurui Liu, Haidong Xue, Dong Liang, Jigao Fu, Wu YanBiao, Yuanyi Zhen, Fengli Xu, Yong Li
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
Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete evidence.
arXiv:2607. 05456v1 Announce Type: new Abstract: While recent advances in large language models have enabled end-to-end automated manuscript generation, existing systems suffer from three critical deficiencies: (i) generated claims are not deterministically grounded in verifiable literature, (ii) experimental results are frequently fabricated rather than executed, and (iii) there exists no standardized, multi-dimensional framework to assess whether AI-generated manuscripts meet the quality and rigor required for real-world publication.
By Ramsha Kamran, Maheera Amjad, Zartasha Mustansar, Arsalan Shaukat, Salma Sherbaz, Muhammad U. S. Khan
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
arXiv:2606. 01013v1 Announce Type: new Abstract: Research is advancing faster than ever with artificial intelligence (AI); and so are the corresponding research papers.
By Di Wu