The Download: an exclusive chat with Jim O'Neill, and the surprising truth about heists Over the past year, Jim O'Neill has become one of the most powerful people in public health. As the US deputy health secretary, he holds two roles at the top of the country's federal health and science agencies. He oversees a department with a budget of over a trillion dollars. And he signed the decision memorandum on the US's deeply controversial new vaccine schedule. In an exclusive interview with earlier this month, O'Neill described his plans to increase human healthspan through longevity-focused research supported by ARPA-H, a federal agency dedicated to biomedical breakthroughs. Fellow longevity enthusiasts said they hope he will bring attention and funding to their cause.

The effectiveness of existing defenses is susceptible to the latest model poisoning attacks, leading to a decrease in prediction accuracy.

Experts worry it will be used to further Robert F. Kennedy Jr.'s anti-vaccine agenda. Get your news from a source that's not owned and controlled by oligarchs. The US Department of Health and Human Services (HHS) is developing a generative artificial intelligence tool to find patterns across data reported to a national vaccine monitoring database and to generate hypotheses on the negative effects of vaccines, according to an inventory released last week of all use cases the agency had for AI in 2025. The tool has not yet been deployed, according to the HHS document, and an AI inventory report from the previous year shows that it has been in development since late 2023. But experts worry that the predictions it generates could be used by HHS secretary Robert F. Kennedy Jr. to further his anti-vaccine agenda.

Experts worry Robert F. Kennedy Jr.'s Health Department will use an internal AI tool to analyze vaccine injury claims in a way that furthers his anti-vaccine agenda. The US Department of Health and Human Services is developing a generative artificial intelligence tool to find patterns across data reported to a national vaccine monitoring database and to generate hypotheses on the negative effects of vaccines, according to an inventory released last week of all use cases the agency had for AI in 2025. The tool has not yet been deployed, according to the HHS document, and an AI inventory report from the previous year shows that it has been in development since late 2023. But experts worry that the predictions it generates could be used by Health and Human Services secretary Robert F. Kennedy Jr. to further his anti-vaccine agenda. A long-standing vaccine critic, Kenedy has upended the childhood vaccination schedule in his year in office, removing several shots from a list of recommended immunizations for all children, including those for Covid-19, influenza, hepatitis A and B, meningococcal disease, rotavirus, and respiratory syncytial virus, or RSV.

RFK's Overhauled Autism Committee Is Even Worse Than It Looks Kennedy has stacked another HHS panel with his fellow travelers in the anti-vaccine and pseudoscience world. Get your news from a source that's not owned and controlled by oligarchs. Last April, Health and Human Services Secretary Robert F. Kennedy, Jr. promised that his agency would find the cause of autism "by September." That didn't pan out, but this week he appears to be trying again--by stacking a decades-old committee devoted to "innovations in autism research, diagnosis, treatment, and prevention" with his friends and fellow travelers in the anti-vaccine and pseudoscience world. Much like the Centers for Disease Control and Prevention's Advisory Committee on Immunization Practices, which Kennedy overhauled last fall with a full slate of new appointees after firing all the old members, he filled the Interagency Autism Coordinating Committee (IACC), which was first established in 2000 to help set the federal agenda for autism research, with Kennedy's allies in the anti-vaccine movement.

The new paradigm of fine-tuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the fine-tuning to produce an alignment-broken model. We conduct an empirical analysis and uncovera \textit{harmful embedding drift} phenomenon, showing a probable cause of the alignment-broken effect. Inspired by our findings, we propose Vaccine, a perturbation-aware alignment technique to mitigate the security risk of users fine-tuning. The core idea of Vaccine is to produce invariant hidden embeddings by progressively adding crafted perturbation to them in the alignment phase. This enables the embeddings to withstand harmful perturbation from un-sanitized user data in the fine-tuning phase. Our results on open source mainstream LLMs (e.g., Llama2, Opt, Vicuna) demonstrate that Vaccine can boost the robustness of alignment against harmful prompts induced embedding drift while reserving reasoning ability towards benign prompts.

AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine development. Because the molecules are governed by physical laws, a key challenge is to incorporate prior information into the training procedure to generate high-quality and realistic molecules. We propose a simple and novel approach to steer the training of diffusion-based generative models with physical and statistics prior information. This is achieved by constructing physically informed diffusion bridges, stochastic processes that guarantee to yield a given observation at the fixed terminal time. We develop a Lyapunov function based method to construct and determine bridges, and propose a number of proposals of informative prior bridges for both high-quality molecule generation and uniformity-promoted 3D point cloud generation. With comprehensive experiments, we show that our method provides a powerful approach to the 3D generation task, yielding molecule structures with better quality and stability scores and more uniformly distributed point clouds of high qualities.

A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent transformer architectures. We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns. We exploit this high storage capacity of modern Hopfield networks to solve a challenging multiple instance learning (MIL) problem in computational biology: immune repertoire classification. In immune repertoire classification, a vast number of immune receptors are used to predict the immune status of an individual. This constitutes a MIL problem with an unprecedentedly massive number of instances, two orders of magnitude larger than currently considered problems, and with an extremely low witness rate. Accurate and interpretable machine learning methods solving this problem could pave the way towards new vaccines and therapies, which is currently a very relevant research topic intensified by the COVID-19 crisis.

Model poisoning attacks greatly jeopardize the application of federated learning (FL). The effectiveness of existing defenses is susceptible to the latest model poisoning attacks, leading to a decrease in prediction accuracy. Besides, these defenses are intractable to distinguish benign outliers from malicious gradients, which further compromises the model generalization. In this work, we propose a novel defense including detection and aggregation, named RECESS, to serve as a "vaccine" for FL against model poisoning attacks. Different from the passive analysis in previous defenses, RECESS proactively queries each participating client with a delicately constructed aggregation gradient, accompanied by the detection of malicious clients according to their responses with higher accuracy. Further, RECESS adopts a newly proposed trust scoring based mechanism to robustly aggregate gradients. Rather than previous methods of scoring in each iteration, RECESS takes into account the correlation of clients' performance over multiple iterations to estimate the trust score, bringing in a significant increase in detection fault tolerance. Finally, we extensively evaluate RECESS on typical model architectures and four datasets under various settings including white/black-box, cross-silo/device FL, etc. Experimental results show the superiority of RECESS in terms of reducing accuracy loss caused by the latest model poisoning attacks over five classic and two state-of-the-art defenses.