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01Discovery · OpenAI
OpenAI’s massive new math drop: [This essay was written in extreme haste before a very long wifi-less flight; please forgive typos.] Part One: My take The real news here isn’t the result; it’s not what we were told. 1. AI once tried to be a science. Now we get stuff like the completely vague report from OpenAI below: “Same procedure”? “Using an unreleased model”? This would never pass peer review. We don’t know what the procedure was. We know nothing about the architecture. For esxample, were the proofs generated in one shot, and then verified by the symbolic system Lean? Was there an iterative process?) We know nothing about the failure rate. We know nothing about the training/post training/data augmention. 2. As a result, we have zero idea of how generalizable the result is outside math. 3. A lot of the discussion on social media has been reduced to an ignorant cheering section that applauds without knowing what it is applauding or what it might mean— without ever asking basic scientific questions. The new system could be a legitimate step toward AGI. Or it could just be a clever leveraging of Lean and synthetic data in a verifiable domain with no generality whatsoever. From the initial report, we can tell almost nothing. §
Discovery · XtalPi
image credit- shutterstock China's XtalPi Holdings, an AI and robotics-driven drug and materials discovery company, has debuted Kodexia™, the industry's first closed-loop small interfering RNA (siRNA) discovery platform combining generative AI, first-principles-driven biological modeling, and automated experimentation. Validating the rapid execution capabilities of this new infrastructure, the company simultaneously announced the advancement of six proprietary therapeutic programs, marking the successful expansion of XtalPi's technology into nucleic acid therapeutics. Kodexia™ also globally co-optimises dual-target siRNAs as single molecules, creating a strategy for disease biology that may not be adequately addressed through single-target intervention. The platform's lead programme in immunoglobulin A (IgA) nephropathy serves as an early empirical validation of this unified architecture. programme is on track for preclinical candidate selection within a nine-month project timeline, significantly accelerating the industry norm of 12 to 18 months. Kodexia™ currently drives six proprietary programmes across metabolic, renal, respiratory, and central nervous system diseases, with more than half having already completed in vivo efficacy evaluations. - China's InnoCare inks $3.5 B deal w...28 September 2026 - Novo Nordisk receives Singapore app...24 September 2026 - Ansell injects $60 M in India to es...21 September 2026 - Avantor expands RIM single-use port...17 September 2026
Nature Portfolio · Nature
As AI agents’ ability to do science autonomously continues to improve, scientists are facing a once-unthinkable question: will I get scooped by a bot? At least twice in the past five weeks, researchers have said that they had been working for an extended period on a particular research question, only to learn that artificial-intelligence companies had either answered the question before them or announced results before they were ready to do the same. In response, researchers who are worried that AI tools might scrape their unpublished work and scoop them are limiting their use of such tools. Anthropic’s AI biolab finds ‘CRISPR-like’ DNA in viruses. What’s next? Nevertheless, some AI researchers, although not all, say that AI agents are increasingly likely to reach conclusions before scientists can do so. Researchers are “going to be scooped, but not because they’ve uploaded a manuscript”, says Geoffrey Irving, chief scientist at Resolution, an AI-safety research organization in Berkeley, California. “They’re going to get scooped because the AIs are very good at solving problems, and they’re going to get better and better.” Controversial findings On 7 September, Tristan Buckmaster, a mathematician at New York University in New York City, posted online that he and a collaborator had made progress on the Navier–Stokes problem, a long-standing open question in fluid dynamics. According to Buckmaster’s account, he and Levent Alpöge, a mathematician at the AI company Anthropic, based in San Francisco, California, whose work with Buckmaster was a personal project, had been pursuing the problem for a year. Buckmaster’s 7 September post also included a paper with the solution to a simpler version of the problem. The next day, 8 September, OpenAI, also in San Francisco, announced that its agents had solved the puzzle — a timing that Buckmaster has publicly questioned. Who gets credit in the AI era? OpenAI maths bombshell sparks debate Buckmaster raised the possibility that information that he and Alpöge had uploaded to an OpenAI tool could have been used to train the company’s models. OpenAI disputed this scenario. The company said in a statement that an investigation had confirmed that Buckmaster’s “prompts” in the two months before the 8 September announcement “could not have influenced the system in any way, including through training”. The statement also said tha…
Hugging Face Daily Papers
Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited. Recent large language model (LLM) agents offer a new way to approach BBO by combining task semantics, computation, optimization tools, and feedback-driven decision making, showing great potential due to the integration with mathematically rigorous tools. However, existing agentic BBO studies use different task domains and system configurations, making their results difficult to compare and the effects of individual design choices hard to isolate. We therefore introduce AgenticBBO-Bench, a cross-domain benchmark for agentic BBO spanning synthetic functions, hyperparameter optimization, database tuning, chip design, and molecular design under a unified finite-budget evaluation protocol. In our experiments, agentic BBO achieves higher family-averaged scores than direct LLM-based methods in all five domains and outperforms the best numerical optimizers in four. We further study three factors shaping agent performance: optimization tools, task information and prior knowledge, and the role of the LLM during search. Our results show that additional numerical tools do not consistently improve performance, task semantics are broadly useful while more specific priors are less reliable, and numerical optimizers can effectively absorb gains from search trajectories established by the agent. Finally, we introduce a five-task frontier challenge within AgenticBBO-Bench and evaluate seven LLMs under the Codex agent harness, where GPT-6 Astra and DeepSeek-V4.1-Flash lie on the Pareto frontier of performance and cost among the evaluated models. Our code is available at https://github.com/lamda-bbo/agentic-bbo.
Discovery · Anthropic
Why is Anthropic running a biology lab? Anthropic is best known for Claude, the artificial intelligence chatbot that competes with OpenAI’s ChatGPT and Google’s Gemini. But the AI company now employs molecular biologists, studies DNA, and operates a laboratory where scientists conduct physical experiments. The San Francisco Bay Area facility is what researchers call a “wet lab”: a laboratory where scientists work directly with biological materials, rather than limiting their research to computer models and simulations. For an AI company, that may sound like an unusual expansion. Anthropic says there is a straightforward reason for it: if Claude is going to help scientists make discoveries about biology, those discoveries eventually have to be tested in the physical world. The company formed its life sciences research group in the spring of 2026 to investigate whether general-purpose AI models could systematically speed up biological discovery. Anthropic says building its own laboratory allowed the same team to work across the entire process, from improving Claude’s biological reasoning to testing its hypotheses experimentally. The project has already produced an early test case. Anthropic said on September 23 that Claude had identified a previously uncharacterised enzyme system associated with unusual repeating DNA sequences. The company says the pattern has some similarities to CRISPR systems, although researchers do not yet know what the newly identified system does. The finding has been released as a preprint and still requires further scientific investigation. Why does an AI company need a physical lab? AI can search scientific literature, analyse enormous datasets, write computer code, and propose explanations for patterns it finds. But biology ultimately depends on what happens inside cells, proteins, DNA, and other physical systems. A model might predict that a particular protein performs a certain function, for example, but scientists still need laboratory experiments to establish whether that prediction is correct. That creates a bottleneck for companies trying to use AI for science: computers can generate hypotheses far more rapidly than researchers can experimentally verify them. Anthropic’s lab is intended partly to close that gap. Its scientists use Claude to search large collections of genetic information for proteins or biological systems tha…
