Google DeepMind disbands its Nobel-prize winning AlphaFold team - Engadget

DeepMind's award-winning AlphaFold team is no more.

arXiv welcomes inaugural CEO and Board of Directors

We have exciting news to share today: the appointment of Dr. Penelope Lewis as arXiv’s inaugural Chief Executive Officer and the establishment of our…

Accelerating scientific discovery with ChatGPT for Academic Researchers

OpenAI is giving 100,000 academic researchers free access to ChatGPT's most advanced AI models to accelerate scientific research, collaboration, and discovery.

Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence

Rui Meng, Research Scientist, and Tomas Pfister, Director, Google Cloud

Verification in the Age of AI Scientists

AI Scientists now operate at a scale that outpaces human capacity for manual review. Systems such as Sakana’s AI Scientist write entire workshop papers end-to-end. Lila Sciences runs autonomous ``AI Science Factories’’ that hypothesize, experiment, and iterate without human guidance. FutureHouse’s Kosmos and Robin generate thousands of candidate hypotheses in a single run, and Google’s Co-Scientist proposes testable experiments at a rate no laboratory can fully evaluate. Each of these systems emphasizes verified results, yet that standard ranges from near-perfect formal proof in mathematics to decade-long clinical trials in medicine, with no shared framework for judging sufficiency across domains. As outputs scale beyond what humans can manually inspect, the question of which results to trust becomes as hard as generating them. The bottleneck for AI for Science is no longer hypothesis generation, it is verification.

Mathematician catches AI-generated research error by OpenAI president | Benjamin Golub posted on the topic | LinkedIn

In case you think the problem of science slop is hypothetical, here's something that happened on X yesterday: The president of OpenAI sharing wrong science slop as proud evidence of the power of mathematical AI. Within a day, the mathematician Tony Feng found a gap that undercut the paper's central argument; the author conceded, and (to his credit!) Brockman deleted the tweet as soon as he became aware. Feng was a hero to catch this. The author of the refuted claim is a mathematician but a self-described outsider to this area, with a prior big claim posted and quickly withdrawn. Greg Brockman is not an expert, either. Neither could check what they were amplifying. Heroes catching errors one at a time won't hold against the volume of AI-generated research now coming. We need an automated, independent verification layer for knowledge work, which is what we're building at Refine (refine.ink). Passing such verification should become a necessary step before big claims, especially outside one's own expertise. A link to a bigger-picture post on this is in the comments. | 23 comments on LinkedIn

ReviewBench: An Extensible Framework for Benchmarking Human and AI Manuscript Review

The volume of scientific manuscripts is rising faster than the available pool of expert reviewers, and AI tools are emerging as a possible response, ranging from frontier large language models applied directly to peer review to purpose-built multi-agent systems. Scalable, standardized benchmarks are needed to regularly evaluate how these tools compare to one another and to human reviewers. We present ReviewBench, an open-source, venue-agnostic framework that compares human and AI reviews across structure, alignment with a paper’s major claims, impact, and critique category. We apply ReviewBench to 145,021 review comments from human reviewers, frontier large language models (GPT-5.2, and Gemini 3 Pro), and Reviewer3.com (R3), a multi-agent peer review system. The dataset spans papers in computer science (ICLR 2025, n = 1,000), social science (Nature Human Behaviour, n = 142), and life science (eLife, n = 1,000). Across disciplines, AI reviews are more structured and engage more directly with a paper’s major claims, with R3 more often surfacing consequential comments, defined as comments capable of undermining those claims. When restricting to critical comments, however, human reviewers rank first on consequential rate on more individual papers than any AI source, despite a lower average. We identify a bimodal reviewer distribution with peaks near 0% and 100%, indicating that many reviewers outperform AI on this metric, but a substantial fraction of reviewers near 0% brings the average down. Critique typing demonstrates systematic differences, where humans emphasize contribution and clarity, while AI emphasizes validity, sufficiency, and transparency. Together, these findings argue against framing AI as a replacement for human review and instead support a complementary model in which AI scales technical verification of major claims while human judgment remains essential for evaluating contribution and shaping editorial decisions.

reviewer3

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

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