No. 028: arXiv rate limit, AI paper mills, Google FluSight - The Hallway Track

The Hallway Track No. 028 - How AI is affecting the practice of science

ArXiv to Ban Researchers for a Year if They Submit AI Slop

The change comes as arXiv and others struggle to manage an influx of AI-generated materials masquerading as rigorous science.

Fair Moderation, Equitable Access, and AI: arXiv’s Updated Rate Limit Policy

arXiv, and the scientific community at large, are facing a watershed moment. Scholarly publishing is currently changing at a rapid pace, and we are seeing a…

Is the scientific paper still a fraud?

How we write scientific papers does not reflect how we do science. Their formal structure infers a pre-ordained linear process rather than reflecting the messy creativity of research. This matters in the AI age because it masks the human in the process.

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.

The fall of the theorem economy

How AI could destroy mathematics and barely touch it

The fall of the theorem economy

How AI could destroy mathematics and barely touch it

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

Early science acceleration experiments with GPT-5

AI models like GPT-5 are an increasingly valuable tool for scientists, but many remain unaware of the capabilities of frontier AI. We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research across mathematics, physics, astronomy, computer science, biology, and materials science. In these examples, the authors highlight how AI accelerated their work, and where it fell short; where expert time was saved, and where human input was still key. We document the interactions of the human authors with GPT-5, as guiding examples of fruitful collaboration with AI. Of note, this paper includes four new results in mathematics (carefully verified by the human authors), underscoring how GPT-5 can help human mathematicians settle previously unsolved problems. These contributions are modest in scope but profound in implication, given the rate at which frontier AI is progressing.

Guest Post — When Errors Become Consensus: Science’s Self-Correction Can No Longer Keep Up - The Scholarly Kitchen

From 19th-century beef tea to paper mills and generative AI, the challenge is no longer detection alone; it is preventing bad claims from becoming embedded knowledge.

AI for Research | Scite

Scite searches 280M+ scholarly articles to give you citation-backed answers and show you how every claim is supported or disputed.

No. 011: Sum-product conjecture disproof, AI-generated authorship fraud, OMB grant-cost rules - The Hallway Track

The Hallway Track No. 011 - How AI is affecting the practice of science

Navier-Stokes lost in translation: Why Lean verification of AI...

Autoformalisation is increasingly used to verify mathematical texts, including those generated by AI, as in OpenAI's announced proof of blow-up of solutions to the Navier-Stokes equations. In this...

No. 001: arXiv goes independent, AI Scientist v2, Claude Scholar - The Hallway Track

The Hallway Track No. 001 - How AI is affecting the practice of science

AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge

While current AI-driven methods excel at deriving empirical models from individual experiments, a significant challenge remains in uncovering the common fundamental physics that underlie these models -- a task at which human physicists are adept. To bridge this gap, we introduce AI-Newton, a novel framework for concept-driven scientific discovery. Our system autonomously derives general physical laws directly from raw, multi-experiment data, operating without supervision or prior physical knowledge. Its core innovations are twofold: (1) proposing interpretable physical concepts to construct laws, and (2) progressively generalizing these laws to broader domains. Applied to a large, noisy dataset of mechanics experiments, AI-Newton successfully rediscovers foundational and universal laws, such as Newton's second law, the conservation of energy, and the universal gravitation. This work represents a significant advance toward autonomous, human-like scientific discovery.