FMMD is a multimodal, multidisciplinary dataset of open peer reviews from F1000Research that pairs manuscript-level visual and structural data with version‑specific reviewer reports and editorial decisions. It addresses key gaps in existing datasets by preserving precise alignment between review comments and the exact manuscript version, and by including a wide range of scientific disciplines beyond computer science. The dataset supports tasks such as visual‑semantic consistency classification, figure‑related review comment generation, and editorial decision prediction, providing a comprehensive empirical resource for multimodal automated scholarly paper review research.
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:2608. 03581v1 Announce Type: cross Abstract: AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are.
AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are. We address these questions by first surveying reviewer-facing AI policies across 111 leading AI/NLP conferences and medical journals, revealing substantial regulation differences between the two communities.
arXiv:2607. 27066v1 Announce Type: cross Abstract: Scientific figure assessment in peer review differs fundamentally from general image quality evaluation: a figure must be visually legible, faithfully support the manuscript's claims, and communicate evidence with a clear visual hierarchy.
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:2506. 08134v4 Announce Type: replace Abstract: Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale.
FIRSTPASS is a large-scale peer review dataset that captures complete multi-round editorial dialogues from a multidisciplinary high-impact journal, Nature Communications. It contains 3,668 records across five scientific domains—biology, chemistry, neuroscience, physics, and earth science—encompassing initial referee reports, author responses, and updated reviewer assessments. Each record is labeled with an outcome (STANDARD or EXTENDED) based on editorial decisions, and the dataset includes detailed parsing pipelines and evaluation scripts for reproducible AI benchmarking.
arXiv:2608. 14625v1 Announce Type: cross Abstract: Academic peer review is under mounting strain: NeurIPS 2025 received 21,575 submissions, ICLR 2025 received 11,603, and ICML 2025 received 12,107.
ReGround is a new large‑scale dataset that links 10,267 reviewer comments to 16,274 pieces of evidence across 3,656 anonymous scientific papers, addressing the challenge of grounding comments in long multimodal documents. The dataset is constructed by leveraging explicit references in author rebuttals, providing high‑precision annotations. Evaluation shows that simple retrieval over full paper text performs poorly, evidence‑type inference is a major bottleneck, and multimodal evidence offers complementary signals that pure text misses.
arXiv:2608.20488v1 Announce Type: new Abstract: AI tools increasingly support tasks across the scientific research cycle, from experiment design and manuscript preparation to peer review. At the same...
arXiv:2607. 27084v1 Announce Type: cross Abstract: Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in scientific papers.
arXiv:2606. 10159v1 Announce Type: cross Abstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage.