Committee

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Putin says organ transplants could grant immortality. Not quite. —Jessica Hamzelou Earlier this week, my editor forwarded me a video of the leaders of Russia and China talking about immortality. “These days at 70 years old you are still a child,” China’s Xi Jinping, 72, was translated as saying. “With the developments of biotechnology, human organs can be continuously transplanted, and people can live younger and younger, and even achieve immortality,” Russia’s Vladimir Putin, also 72, is reported to have replied. In reality, rounds of organ transplantation surgery aren’t likely to help anyone radically extend their lifespan anytime soon. And it’s a simplistic way to think about aging—a process so complicated that researchers can’t agree on what causes it, why it occurs, or even how to define it, let alone “treat” it. Read the full story. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. India is using robots to clean sewer pipes so humans no longer have to When Jitender was a child in New Delhi, both his parents worked as manual scavengers—a job that involved clearing the city’s sewers by hand. Now, he is among almost 200 contractors involved in the Delhi government’s effort to shift from this manual process to safer mechanical methods. Although it has been outlawed since 1993, manual scavenging—the practice of extracting human excreta from toilets, sewers, or septic tanks—is still practiced widely in India. And not only is the job undignified, but it can be extremely dangerous. Now, several companies have emerged to offer alternatives at a wide range of technical complexity. Read the full story. —Hamaad Habibullah This story is from our new print edition, which is all about the future of security. Subscribe here to catch future copies when they land. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 RFK Jr buried a major study linking alcohol and cancerClearly, the alcohol industry’s intense lobbying of the Trump administration is working. (Vox)+ RFK Jr repeated health untruths during a marathon Senate hearing yesterday. (Mother Jones)+ His anti-vaccine stance alarmed Democrats and Republicans alike. (The Atlantic $) 2 US tech giants want to embed AI in educationThey’re backing a vaguely worded initiative to that effect launched by Melania Trump. (Rolling Stone $)+ Tech leaders took it in turns to praise Trump during dinner. (WSJ $)+ Elon Musk was nowhere to be seen. (The Guardian)+ AI’s giants want to take over the classroom. (MIT Technology Review) 3 The FTC will probe AI companies over their impact on children In a bid to evaluate whether chatbots are harming their mental health. (WSJ $)+ An AI companion site is hosting sexually charged conversations with underage celebrity bots. (MIT Technology Review) 4 Podcasting giant Joe Rogan has been spreading climate misinformationHe’s grossly misinterpreted scientists’ research—and they’re exasperated. (The Guardian)+ Rogan claims the Earth’s temperature is plummeting. It isn’t.  (Forbes)+ Why climate researchers are taking the temperature of mountain snow. (MIT Technology Review) 5 DeepSeek is working on its own advanced AI agentWatch out, OpenAI. (Bloomberg $) 6 OpenAI will start making its own AI chips next yearIn a bid to lessen its reliance on Nvidia. (FT $) 7 Warner Bros is suing MidjourneyThe AI startup used the likenesses of characters including Superman without permission, it alleges. (Bloomberg $)+ What comes next for AI copyright lawsuits? (MIT Technology Review) 8 Rivers and lakes are being used to cool down buildingsBut networks in Paris, Toronto, the US are facing a looming problem. (Wired $)+ The future of urban housing is energy-efficient refrigerators. (MIT Technology Review) 9 How high school reunions survive in the age of social mediaCuriosity is a powerful driving force, it seems. (The Atlantic $) 10 Facebook’s poke feature is back If I still used Facebook, I’d be thrilled. (TechCrunch) Quote of the day “Even if it doesn’t turn you into the alien if you eat this stuff, I guarantee you’ll grow an extra ear.” —Senator John Kennedy, a Republican from Louisiana, warns of dire consequences if Americans eat shrimp from countries other than the US, Gizmodo reports. One more thing Why one developer won’t quit fighting to connect the US’s gridsMichael Skelly hasn’t learned to take no for an answer. For much of the last 15 years, the energy entrepreneur has worked to develop long-haul transmission lines to carry wind power across the Great Plains, Midwest, and Southwest. But so far, he has little to show for the effort. Skelly has long argued that building such lines and linking together the nation’s grids would accelerate the shift from coal- and natural-gas-fueled power plants to the renewables needed to cut the pollution driving climate change. But his previous business shut down in 2019, after halting two of its projects and selling off interests in three more. Skelly contends he was early, not wrong. And he has a point: market and policymakers are increasingly coming around to his perspective. Read the full story. —James Temple We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + The Paper, the new mockumentary from the makers of the American Office, looks interesting.+ Giorgio Armani was a true maestro of menswear.+ The phases of the moon are pretty fascinating + The Damien Hirst-directed video for Blur’s classic Country House has been given a 4K makeover.

Table of contents What is a Personal Health Agent? How does the PHA framework operate? How was the PHA evaluated? Evaluation of the Data Science Agent Evaluation of the Domain Expert Agent Evaluation of the Health Coach Agent Evaluation of the Integrated PHA System How does the PHA contribute to health AI? What is the larger significance of Google’s PHA blueprint? Conclusion https://arxiv.org/abs/2508.20148v1 What is a Personal Health Agent? Large language models (LLMs) have demonstrated strong performance across various domains like clinical reasoning, decision support, and consumer health applications. However, most existing platforms are designed as single-purpose tools, such as symptom checkers, digital coaches, or health information assistants. These approaches often fail to address the complexity of real-world health needs, where individuals require integrated reasoning over wearable streams, personal health records, and laboratory test results. A team of researchers from Google has proposed a Personal Health Agent (PHA) framework. The PHA is designed as a multi-agent system that unifies complementary roles: data analysis, medical knowledge reasoning, and health coaching. Instead of returning isolated outputs from a single model, the PHA employs a central orchestrator to coordinate specialized sub-agents, iteratively synthesize their outputs, and deliver coherent, personalized guidance. https://arxiv.org/abs/2508.20148v1 How does the PHA framework operate? The Personal Health Agent (PHA) is built on top of the Gemini 2.0 model family. It follows a modular architecture consisting of three sub-agents and one orchestrator: Data Science Agent (DS)The DS agent interprets and analyzes time-series data from wearables (e.g., step counts, heart rate variability, sleep metrics) and structured health records. It is capable of decomposing open-ended user questions into formal analysis plans, executing statistical reasoning, and comparing results against population-level reference data. For example, it can quantify whether physical activity in the past month is associated with improvements in sleep quality. Domain Expert Agent (DE)The DE agent provides medically contextualized information. It integrates personal health records, demographic information, and wearable signals to generate explanations grounded in medical knowledge. Unlike general-purpose LLMs that may produce plausible but unreliable outputs, the DE agent follows an iterative reasoning-investigation-examination loop, combining authoritative medical resources with personal data. This allows it to provide evidence-based interpretations, such as whether a specific blood pressure measurement is within a safe range for an individual with a particular condition. Health Coach Agent (HC)The HC agent addresses behavioral change and long-term goal setting. Drawing from established coaching strategies such as motivational interviewing, it conducts multi-turn conversations, identifies user goals, clarifies constraints, and generates structured, personalized plans. For example, it may guide a user through setting a weekly exercise schedule, adapting to individual barriers, and incorporating feedback from progress tracking. OrchestratorThe orchestrator coordinates these three agents. When a query is received, it assigns a primary agent responsible for generating the main output and supporting agents to provide contextual data or domain knowledge. After collecting the results, the orchestrator runs an iterative reflection loop, checking outputs for coherence and accuracy before synthesizing them into a single response. This ensures that the final output is not merely an aggregation of agent responses but an integrated recommendation. How was the PHA evaluated? The research team conducted one of the most comprehensive evaluations of a health AI system to date. Their evaluation framework involved 10 benchmark tasks, 7,000+ human annotations, and 1,100 hours of assessment from health experts and end-users. Evaluation of the Data Science Agent The DS agent was assessed on its ability to generate structured analysis plans and produce correct, executable code. Compared to baseline Gemini models, it demonstrated: A significant increase in analysis plan quality, improving mean expert-rated scores from 53.7% to 75.6%. A reduction in critical data handling errors from 25.4% to 11.0%. An improvement in code pass rates from 58.4% to 75.5% on first attempts, with further gains under iterative self-correction. https://arxiv.org/abs/2508.20148v1 https://arxiv.org/abs/2508.20148v1 https://arxiv.org/abs/2508.20148v1 Evaluation of the Domain Expert Agent The DE agent was benchmarked across four capabilities: factual accuracy, diagnostic reasoning, contextual personalization, and multimodal data synthesis. Results include: Factual knowledge: On over 2,000 board-style exam questions across endocrinology, cardiology, sleep medicine, and fitness, the DE agent achieved 83.6% accuracy, outperforming baseline Gemini (81.8%). Diagnostic reasoning: On 2,000 self-reported symptom cases, it achieved 46.1% top-1 diagnostic accuracy compared to 41.4% for a state-of-the-art Gemini baseline. Personalization: In user studies, 72% of participants preferred DE agent responses to baseline outputs, citing higher trustworthiness and contextual relevance. Multimodal synthesis: In expert clinician reviews of health summaries generated from wearable, lab, and survey data, the DE agent’s outputs were rated more clinically significant, comprehensive, and trustworthy than baseline outputs. Evaluation of the Health Coach Agent The HC agent was designed and assessed through expert interviews and user studies. Experts emphasized the need for six coaching capabilities: goal identification, active listening, context clarification, empowerment, SMART (Specific, Measurable, Attainable, Relevant, Time-bound) recommendations, and iterative feedback incorporation. In evaluations, the HC agent demonstrated improved conversation flow and user engagement compared to baseline models. It avoided premature recommendations and instead balanced information gathering with actionable advice, producing outputs more consistent with expert coaching practices. Evaluation of the Integrated PHA System At the system level, the orchestrator and three agents were tested together in open-ended, multimodal conversations reflecting realistic health scenarios. Both experts and end-users rated the integrated Personal Health Agent (PHA) significantly higher than baseline Gemini systems across measures of accuracy, coherence, personalization, and trustworthiness. How does the PHA contribute to health AI? The introduction of a multi-agent PHA addresses several limitations of existing health AI systems: Integration of heterogeneous data: Wearable signals, medical records, and lab test results are analyzed jointly rather than in isolation. Division of labor: Each sub-agent specializes in a domain where single monolithic models often underperform, e.g., numerical reasoning for DS, clinical grounding for DE, and behavioral engagement for HC. Iterative reflection: The orchestrator’s review cycle reduces inconsistencies that often arise when multiple outputs are simply concatenated. Systematic evaluation: Unlike most prior work, which relied on small-scale case studies, the Personal Health Agent (PHA) was validated with a large multimodal dataset (the WEAR-ME study)

Table of contents The Growing Role of AI in Biomedical Research The Core Challenge: Matching Expert-Level Reasoning Why Traditional Approaches Fall Short Biomni-R0: A New Paradigm Using Reinforcement Learning Training Strategy and System Design Results That Outperform Frontier Models Designing for Scalability and Precision Key Takeaways from the research include: The Growing Role of AI in Biomedical Research The field of biomedical artificial intelligence is evolving rapidly, with increasing demand for agents capable of performing tasks that span genomics, clinical diagnostics, and molecular biology. These agents aren’t merely designed to retrieve facts; they are expected to reason through complex biological problems, interpret patient data, and extract meaningful insights from vast biomedical databases. Unlike general-purpose AI models, biomedical agents must interface with domain-specific tools, comprehend biological hierarchies, and simulate workflows similar to those of researchers to effectively support modern biomedical research. The Core Challenge: Matching Expert-Level Reasoning However, achieving expert-level performance in these tasks is far from trivial. Most large language models fall short when dealing with the nuance and depth of biomedical reasoning. They may succeed on surface-level retrieval or pattern recognition tasks, but often fail when challenged with multi-step reasoning, rare disease diagnosis, or gene prioritization, areas that require not just data access, but contextual understanding and domain-specific judgment. This limitation has created a clear gap: how to train biomedical AI agents that can think and act like domain experts. Why Traditional Approaches Fall Short While some solutions leverage supervised learning on curated biomedical datasets or retrieval-augmented generation to ground responses in literature or databases, these approaches have drawbacks. They often rely on static prompts and pre-defined behaviors that lack adaptability. Furthermore, many of these agents struggle to effectively execute external tools, and their reasoning chains collapse when faced with unfamiliar biomedical structures. This fragility makes them ill-suited for dynamic or high-stakes environments, where interpretability and accuracy are non-negotiable. Biomni-R0: A New Paradigm Using Reinforcement Learning Researchers from Stanford University and UC Berkeley introduced a new family of models called Biomni-R0, built by applying reinforcement learning (RL) to a biomedical agent foundation. These models, Biomni-R0-8B and Biomni-R0-32B, were trained in an RL environment specifically tailored for biomedical reasoning, using both expert-annotated tasks and a novel reward structure. The collaboration combines Stanford’s Biomni agent and environment platform with UC Berkeley’s SkyRL reinforcement learning infrastructure, aiming to push biomedical agents past human-level capabilities. Training Strategy and System Design The research introduced a two-phase training process. First, they used supervised fine-tuning (SFT) on high-quality trajectories sampled from Claude-4 Sonnet using rejection sampling, effectively bootstrapping the agent’s ability to follow structured reasoning formats. Next, they fine-tuned the models using reinforcement learning, optimizing for two kinds of rewards: one for correctness (e.g., selecting the right gene or diagnosis), and another for response formatting (e.g., using structured <think> and <answer> tags correctly). To ensure computational efficiency, the team developed asynchronous rollout scheduling that minimized bottlenecks caused by external tool delays. They also expanded the context length to 64k tokens, allowing the agent to manage long multi-step reasoning conversations effectively. Results That Outperform Frontier Models The performance gains were significant. Biomni-R0-32B achieved a score of 0.669, a jump from the base model’s 0.346. Even Biomni-R0-8B, the smaller version, scored 0.588, outperforming general-purpose models like Claude 4 Sonnet and GPT-5, which are both much larger. On a task-by-task basis, Biomni-R0-32B scored highest on 7 out of 10 tasks, while GPT-5 led in 2, and Claude 4 in just 1. One of the most striking results was in rare disease diagnosis, where Biomni-R0-32B reached 0.67, compared to Qwen-32B’s 0.03, a more than 20× improvement. Similarly, in GWAS variant prioritization, the model’s score increased from 0.16 to 0.74, demonstrating the value of domain-specific reasoning. Designing for Scalability and Precision Training large biomedical agents requires dealing with resource-heavy rollouts involving external tool execution, database queries, and code evaluation. To manage this, the system decoupled environment execution from model inference, allowing more flexible scaling and reducing idle GPU time. This innovation ensured efficient use of resources, even with tools that had varying execution latencies. Longer reasoning sequences also proved beneficial. The RL-trained models consistently produced lengthier, structured responses, which strongly correlated with better performance, highlighting that depth and structure in reasoning are key indicators of expert-level understanding in biomedicine. Key Takeaways from the research include: Biomedical agents must perform deep reasoning, not just retrieval, across genomics, diagnostics, and molecular biology. The central problem is achieving expert-level task performance, mainly in complex areas such as rare diseases and gene prioritization. Traditional methods, including supervised fine-tuning and retrieval-based models, often fall short in terms of robustness and adaptability. Biomni-R0, developed by Stanford and UC Berkeley, uses reinforcement learning with expert-based rewards and structured output formatting. The two-phase training pipeline, SFT followed by RL, proved highly effective in optimizing performance and reasoning quality. Biomni-R0-8B delivers strong results with a smaller architecture, while Biomni-R0-32B sets new benchmarks, outperforming Claude 4 and GPT-5 on 7 of 10 tasks. Reinforcement learning enabled the agent to generate longer, more coherent reasoning traces, a key trait of expert behavior. This work lays the foundation for super-expert biomedical agents, capable of automating complex research workflows with precision. Check out the Technical details. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. The post Biomni-R0: New Agentic LLMs Trained End-to-End with Multi-Turn Reinforcement Learning for Expert-Level Intelligence in Biomedical Research appeared first on MarkTechPost.