arXiv Machine Learning By Anirudh Sundar, Min Chen, Divya Tadimeti, Gemma Zhang, Alice Li, Nigel Boachie Kumankumah, Pavan Uttej Ravva, Sadid Hasan, Somya Chatterjee, Pruthvi Prakash Navada, Xiao Wang, Yue Kang, Sulaiman Vesal, Larry Heck

Metag: A dataset to build agentic meta-reviewing capabilities

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arXiv Computation and Language
2d ago

Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision

The paper introduces Mr Dre, an evaluation suite that tests Deep Research Agents (DRAs) on multi-turn report revision, a process more reflective of human research practices. It defines a unified long‑form report protocol covering comprehensiveness, factuality, and presentation, and includes a human‑verified feedback simulation pipeline. Analysis of five DRAs shows that while agents can address most user feedback, they regress on 16‑28% of previously covered content and citation quality, and over successive turns they disrupt unrelated content and fail to preserve earlier edits, with prompt engineering and dedicated subagents offering limited improvement.

By Bingsen Chen, Boyan Li, Ping Nie, Yuyu Zhang, Xi Ye, Chen Zhao
arXiv AI
Jun 30

meta-pipe: An LLM-agent pipeline for end-to-end automated systematic review and meta-analysis

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
arXiv Computation and Language
2d ago

Can AI Reviewers See Your Efforts to Improve Your Paper? A Large-Scale Study of Paper Revision Trajectories

The study examines whether AI reviewers’ scores reflect authors’ revisions by analyzing 6,959 paper trajectories from five AI conferences. Major revisions that add empirical evidence and improve framing are identified, yet most reviewers do not change scores for these improvements and agreement on score direction is weak. Reviewer-specific associations exist, but overall predictive power is limited, and brief negative comments can negate any scoring gains from substantive revisions.

By Yaochen Wang, Chenguang Wang, Ming Li, Haomin Zhuang, Xinyue Zeng, Han Chen, Kwesi Adu Cobbina, Dang Nguyen, Dianqi Li, Dawei Zhou, Tianyi Zhou
arXiv Computation and Language
Sep 16

PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress

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