A new automated feedback option for authors - openRxiv

Introducing Hypothesis commenting on bioRxiv and medRxiv: an updated way to engage with preprints - openRxiv

An important part of our mission at openRxiv is to collaborate with other organizations to improve science communication and promote interoperability. Preprints are just one part of the research workflow, and we want to ensure they are effectively integrated with the tools and communities that drive scientific progress. Today, we’re excited to share about another…

Launching openRxiv Labs - openRxiv

Over the last thirteen years, bioRxiv and medRxiv have grown into widely used infrastructure for rapid research sharing in biology and medicine. The reliability and researcher-first values that have defined these platforms since their founding remain central to how we operate, and building on the trust, partnerships, and community relationships researchers worldwide depend on is…

The Story behind the Science: Preprints of pandemic potential—how bioRxiv and medRxiv brought preprints to the life sciences

ABSTRACT The bioRxiv and medRxiv preprint servers brought preprinting to the life sciences and played a critical role in disseminating COVID research during the pandemic. Here, I reflect on the birth of bioRxiv and medRxiv and the crucial role so many members of the community played, our experience during the pandemic, and the launch of the new non-profit organization set up to oversee the servers. The pandemic was a stress test for bioRxiv and medRxiv that demonstrated their value and robustness. Under the umbrella of openRxiv, they are now poised to become long-term infrastructure underpinning a new publishing ecosystem.

reviewer3

A command line tool that uses LLMs to give constructive feedback about scientific .docx manuscripts.

Homepage - openRxiv

openRxiv is an independent non-profit, the new organizational home for bioRxiv and medRxiv, enabling researchers to instantly share groundbreaking findings with the global scientific community.

Curvenote Reader brings a better reading experience to half a million openRxiv preprints

Curvenote Reader is now live across half a million bioRxiv preprints - bringing inline citations, zoomable figures, and connected context directly into the reading flow. Find out how we modernized one of science's biggest preprint repositories without disrupting a single workflow.

Making sense of preprints by adding context – The Publish Your Reviews initiative - LSE Impact

Improving scientific publishing is often framed as an issue of openness and speed and less often as one of context. In this post, Ludo Waltman and Jessica Polka make the case for a more contextualised approach to open access publishing and preprinting, and introduce the Publish Your Reviews initiative. Launched today by ASAPbio, the initiative

You can just review things: A digital ethnography of informal peer review

Across scholarly communities, manuscripts face similar evaluative rituals: editors invite experts to privately assess submissions through formal peer reviews. This closed, loosely structured, and...

A Claude skill for pre-submission peer review

Open-source skill to use Claude + Consensus for pre-submission mock peer review backed by citations to published, peer-reviewed literature.

Center for Open Science

At the Center for Open Science, our mission is to increase openness, integrity, and reproducibility of scholarly research. Promoting these practices within the research funding and publishing communities accelerates scientific progress

AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot

Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and timeliness. Recent advances in AI have led the community to consider its use in peer review, yet a key unresolved question is whether AI can generate technically sound reviews at real-world conference scale. Here we report the first large-scale field deployment of AI-assisted peer review: every main-track submission at AAAI-26 received one clearly identified AI review from a state-of-the-art system. The system combined frontier models, tool use, and safeguards in a multi-stage process to generate reviews for all 22,977 full-review papers in less than a day. A large-scale survey of AAAI-26 authors and program committee members showed that participants not only found AI reviews useful, but actually preferred them to human reviews on key dimensions such as technical accuracy and research suggestions. We also introduce a novel benchmark and find that our system substantially outperforms a simple LLM-generated review baseline at detecting a variety of scientific weaknesses. Together, these results show that state-of-the-art AI methods can already make meaningful contributions to scientific peer review at conference scale, opening a path toward the next generation of synergistic human-AI teaming for evaluating research.

Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem

Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale. Exponential growth in manuscript submissions to premier ML venues such as NeurIPS, ICML, and ICLR is outpacing the finite capacity of qualified reviewers, leading to concerns about review quality, consistency, and reviewer fatigue. This position paper argues that AI-assisted peer review must become an urgent research and infrastructure priority. We advocate for a comprehensive AI-augmented ecosystem, leveraging Large Language Models (LLMs) not as replacements for human judgment, but as sophisticated collaborators for authors, reviewers, and Area Chairs (ACs). We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making. Crucially, we contend that the development of such systems hinges on access to more granular, structured, and ethically-sourced peer review process data. We outline a research agenda, including illustrative experiments, to develop and validate these AI assistants, and discuss significant technical and ethical challenges. We call upon the ML community to proactively build this AI-assisted future, ensuring the continued integrity and scalability of scientific validation, while maintaining high standards of peer review.

Open Evaluation: A Vision for Entirely Transparent Post-Publication Peer Review and Rating for Science

The two major functions of a scientific publishing system are to provide access to and evaluation of scientific papers. While open access (OA) is becoming a reality, open evaluation (OE), the other side of coin, has received less attention. Evaluation steers the attention of the scientific community and thus the very course of science. It also influences the use of scientific findings in public policy. The current system of scientific publishing provides only journal prestige as an indication of the quality of new papers and relies on a non-transparent and noisy pre-publication peer review process, which delays publication by many months on average. Here I propose an OE system, in which papers are evaluated post-publication in an ongoing fashion by means of open peer review and rating. Through signed ratings and reviews, scientists steer the attention of their field and build their reputation. Reviewers are motivated to be objective, because low-quality or self-serving signed evaluations will negatively impact their reputation. A core feature of this proposal is a division of powers between the accumulation of evaluative evidence and the analysis of this evidence by paper evaluation functions (PEFs). PEFs can be freely defined by individuals or groups (e.g. scientific societies) and provide a plurality of perspectives on the scientific literature. Simple PEFs will use averages of ratings, weighting reviewers (e.g. by H-factor) and rating scales (e.g. by relevance to a decision process) in different ways. Complex PEFs will use advanced statistical techniques to infer the quality of a paper. Papers with initially promising ratings will be more deeply evaluated. The continual refinement of PEFs in response to attempts by individuals to influence evaluations in their own favor will make the system ungameable. OA and OE together have the power to revolutionize scientific publishing and usher in a new culture of transparency, constructive criticism, and collaboration.

OpenRxiv & Curvenote’s new Reader launch took to the stage at FORCE11 2026

At FORCE11 2026, Curvenote showcased the newly launched Curvenote Reader deployed over all of openRxiv to enthusiastic feedback from scientific publishing leaders — with callouts on the innovation from the opening and closing keynotes as a meaningful step forward for open science.

(PDF) Peer Reviewers Should be Allowed to Upload Manuscripts to AI

PDF | Publishers bar peer reviewers-but not authors or editors-from feeding manuscripts to AI. Cited confidentiality concerns don't survive scrutiny.... | Find, read and cite all the research you need on ResearchGate