Weekly signals · 2026-W38 | Juan Li

AI-generated weekly synthesis · selected by a reproducible rule, not curated by a human · not individually human-reviewed · verify original sources before citing

How fast are you ageing? Ask AI

What changed

Understanding why people age — and how to slow that process down — is an obsession that has stretched across millennia, from mythic quests for immortality to modern science’s hunt for drugs that lengthen lifespan. Now, that quest has entered the AI era. A report1 published today in Cell unveils a bespoke artificial-intelligence system that aims to turbocharge longevity research. The system introduces large language models (LLMs) that the authors trained on ageing-biology data. It also includes a suite of 17 tasks that can serve as benchmarks to gauge how well these and other LLMs perform on ageing-related projects. Finally, it includes an interface that brings the models and other ageing-related research tools together with AI assistants called agents to aid analyses. Gene clock predicts time to death in humans — and assesses ‘biological’ age The authors used their benchmarks to evaluate both their specialist LLMs and much larger cutting-edge commercial models produced by companies including OpenAI and DeepSeek, that are trained on larger, more diverse data sets to handle a broad range of tasks. In many — although not all — of the tests, the LLMs tailored to ageing research outperformed the large LLMs. “The paper makes a very important contribution to the longevity and ageing field,” says Marinka Zitnik, a computer scientist at Harvard Medical School in Boston, Massachusetts, who was not involved in the work. “It can inform the development of next-generation AI models.” The work also tackles a key question for the field: how can researchers train AI tools to understand ageing when scientists have not yet defined the concept for themselves? “For certain disease areas, such as cancer, the AI tasks can be very crisply defined,” says Zitnik. “Ageing is a very different beast. It is hard to define.” Clock collectors Researchers have spent decades gathering data to define that beast. One result has been a slew of ‘clocks’ aimed at measuring biological ageing, or the rate at which a person’s body exhibits signs of ageing. Such clocks measure biological age by assessing characteristics such as facial features, protein levels, gene activity and brain scans, all with the goal of quantifying whether the effects of ageing are outpacing or lagging behind someone’s chronological age in years. How quickly are you ageing? What molecular ‘clocks’ can tell you about your hea…

Sources and evidence

Nature Portfolio · Nature · Original source ↗ · Site entry · 2026-09-17

How a team of AIs discovered a promising lung-cancer drug

What changed

It’s a pharmaceutical-company executive’s dream: tens of thousands of employees working day and night, uninterrupted by meals, sleep or distraction, to find the next blockbuster drug. A sophisticated artificial-intelligence system called the Virtual Biotech, which is described today in the journal Science1, is a step towards this vision. The Virtual Biotech comprises as many as 37,000 agents — AI systems that autonomously interact with large language models (LLMs) or with each other and are capable of performing multistep tasks. It uncovered a molecular signal that could help to predict clinical-trial success. And with some human oversight, it identified a promising lung-cancer treatment. "We want to see how far these agent teams of AI scientists can help us to really accelerate drug discovery and development,” says James Zou, a computer scientist at Stanford University in California who led the effort. But other scientists note that the Virtual Biotech has not been vetted in the crucible of real-world drug discovery, and its predictions were not validated through experiments, let alone clinical trials. Organizational chart In the past year or so, ever-more-capable AI scientists have taken hold in various fields. In biomedicine, they have shown aptitude for complex tasks ranging from genomic data analysis to hypothesis generation and experimental design. To test these systems’ ability to find new drugs, a job that involves numerous interrelated tasks, Zou assembled a team of agents, mirroring the staff of a biotechnology company. In the Virtual Biotech’s set-up, a chief scientific officer (CSO) agent directs ‘employees’ in different divisions, each with their own subspecialities, such as target identification and clinical-trial design. For the Science study, Zhou’s team used versions of Claude — developed by Anthropic in San Francisco, California — as the underlying LLM that powered the agents. But he says that any advanced LLM will do, including open-source models that researchers can run on their own computers. To test the Virtual Biotech’s capabilities, Zou’s team tasked it with analysing the published results of more than 55,000 clinical trials that have been run for drugs for a wide range of conditions. The CSO assigned 37,075 agents to each tackle a single later-stage trial. Other virtual biotech employees searched for predictors of success in data se…

Sources and evidence

Nature Portfolio · Nature · Original source ↗ · Site entry · 2026-09-17

OpenAI's Navier–Stokes claim sparks debate over who gets credit in AI-era mathematics

What changed

OpenAI’s announcement that it used an artificial-intelligence model to solve a major puzzle in fluid dynamics — the Navier–Stokes problem — is likely to change how mathematics is done forever. The claim, made by the AI company in San Francisco, California, on 8 September, has also sparked controversy about whether OpenAI tools have learnt information from human mathematicians who were also racing to solve the problem using AI tools. The widespread use of AI models across most fields of research, and the difficulty of tracing the origin of the information used to train models, could put the very notion of scholarly credit into jeopardy, researchers warn. “It is quite possible that academic researchers have not fully grasped the consequences of uploading data and knowledge to a personal AI model account,” says Luke McDonagh, who studies intellectual-property law at the London School of Economics and Political Science. In an open letter decrying AI companies’ entry into solving mathematical problems, 25 winners of the Fields Medal — which is often considered mathematics’ equivalent to the Nobel Prize — write that AI tools muddy the ability to give appropriate credit: “As in all creative professions, this raises severe attribution and plagiarism questions.” Chatbot controversy The day before OpenAI confirmed rumours that it had solved the Navier–Stokes problem — one of the highest-profile questions in mathematics — one researcher was already raising the alarm. Tristan Buckmaster, a mathematician at New York University in New York City, and his collaborator Levent Alpöge, a mathematician at Harvard University in Cambridge, Massachusetts, had been working on an aspect of the Navier–Stokes problem using tools from OpenAI as well as Anthropic. They were told that OpenAI was preparing to announce that it had solved the problem. Buckmaster wrote on social media that the company had jumped on the problem after hearing about the work by him and Alpöge, and that OpenAI's model could have been learning from their interactions with ChatGPT. An OpenAI spokesperson told Nature: “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.” The company said that it started working on the problem on 1 September and that it had not seen “any of their work through any means until they released it publicly”.…

Sources and evidence

Nature Portfolio · Nature · Original source ↗ · Site entry · 2026-09-17

OpenAI's 88-Hour Math Proof: A Result Being Correct vs. Being Understood

What changed

On September 8, OpenAI said 10,000 of its agents, running for 88 hours, had produced a 166-page proof regarding the Navier-Stokes equations — which describe fluid movement and have remained an open question since 1934. It's one of the seven Millennium Prize Problems established by the Clay Mathematics Institute in 2000, each carrying a $1 million prize; only one has ever been awarded. OpenAI has said it won't be claiming the money, which is just as well, since outside estimates put the compute bill between $10 million and $40 million. Clay now says the problem has "apparently been settled," while the formal evaluation proceeds. The institute calls that process "deliberately unhurried." Before Clay will consider paying, a proof has to be published in a refereed journal of worldwide repute and then survive two years of mathematicians trying to break it. If you check Clay's website, it now labels Navier-Stokes "Active": neither unsolved nor solved. Those conditions were written in 2000, and you'll notice they don't ask whether the argument is formally correct; they ask whether mathematicians have read it, argued with it and failed to break it. It seems the machine has produced the proof much faster than the mathematical community can absorb it, and while the cost of producing the answer is visible, the cost of turning it into understanding is only starting to be calculated. Navier-Stokes is the sharpest case so far, but it isn't really a story about fluid dynamics. The interesting story is the gap between a result being correct and a result being understood. Earlier this week on September 11, 25 Fields Medalists signed a declaration saying the way AI labs chase mathematical problems is damaging mathematics. The next day Terence Tao, who signed it, gave his blog over to two philosophers of mathematics, Silvia De Toffoli and Eamon Duede, who explained the claim. They separated two concepts that had rarely needed separating before: a logical proof (deductive validity, which a machine can verify) and an intelligible proof (understanding why something is true in a form humans can explain and build upon). Historically, these came together because no human could construct the first without possessing the second. AI pulled them apart. Their verdict on the Navier-Stokes result: "What OpenAI has given us is an answer. But it is not clear that they have delivered a fruit…

Sources and evidence

外部发现 · OpenAI · Original source ↗ · Site entry · 2026-09-15

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

What changed

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents 摘要 ScienceBuddy is an interactive scientific research workspace that brings continually improving agents into a researcher's everyday work. It supports researchers in carrying out scientific tasks while turning their requests, feedback and execution evidence into tasks and evaluation rubrics for continual learning. At its core is recursive-in-recursive self-improvement, which couples harness evolution with model reinforcement learning: the inner recursion improves the harness with the model fixed, and the outer recursion trains the model under the improved harness. Case studies cover researcher interaction, harness refinement and model learning across four families of scientific task. 作者 * 共同一作期刊/会议引用 BibTeX @techreport{xue2026sciencebuddy, title = {{ScienceBuddy}: {Recursive-in-Recursive} Self-Improvement for Interactive Scientific Agents}, author = {Xue, Shuhan and Zhong, Jianyuan and Nan, Ziyuan and Li, Wenbin and Yu, Zhaochen and Ding, Jinchao and Gao, Qiang and Zhan, Pengyu and Zhang, Yuntong and Cheng, Tian and Yin, Zhenfei and Wu, Yingcheng and Yang, Ling}, institution = {PhAI Labs}, type = {Technical Report}, number = {PHAI-TR-2026-02}, month = {September}, year = {2026}, url = {https://phai-labs.com/papers/sciencebuddy/}, note = {Version v1. Equal contribution: Shuhan Xue, Jianyuan Zhong, Ziyuan Nan} }

Sources and evidence

PhAI Labs · 技术报告与论文 · Original source ↗ · Site entry · 2026-09-16