arXiv Machine Learning
Metag: A dataset to build agentic meta-reviewing capabilities
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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.
arXiv Computation and Language
3d ago
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.
arXiv AI
Sep 10
Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review
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...
arXiv AI
Jul 1
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
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.
arXiv AI
Jun 6
EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation
arXiv:2606. 06025v1 Announce Type: cross Abstract: Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback.
arXiv AI
Sep 4
More Criticism Does Not Make a Better Review: EquiReview-R
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.