Introducing CUDA Rust! CUDA Rust lets you write GPU kernels natively in Rust, not just launch them from it. Two paths: cuda-oxide for SIMT kernels compiled to PTX, and cutile-rs for Tile-based programming on stable Rust. Both can catch aliasing errors at compile time. Technical blog: nvda.ws/4hm1bHS

Excited to see this out! Relational learning needs strong benchmarks, standardized evaluation, and easy ways to bring methods to real-world relational data.

Fun conversation with @ItaiYanai on @nightsciencepod about what happens when AI starts doing science: agents that find patterns, generate hypotheses, and try to falsify them with more data. We also got into taste, judgment, and where human scientists still matter most. open.spotify.com/episode/0py… podcasts.apple.com/us/podcas…

Exciting milestone for @KexinHuang5 and the @phylo_bio team. Putting AI scientist systems directly in the hands of discovery scientists is where the next big gains in biomedical AI will come from.

Today, we're excited to share that Biomni is published in @ScienceMagazine. Biomedical research is still fragmented, manual, and difficult to scale. In this work, we introduce Biomni - the first general-purpose biomedical AI agent with an integrated biology environment that can reason, plan, and execute end-to-end scientific workflows. We show that, with the right environment and harness, AI can automate large-scale omics analyses, orchestrate laboratory robotics, optimize molecular properties, and even train new AI models for biology. We also introduce a reinforcement learning recipe for continually improving biomedical AI agents, enabling open-source models to achieve frontier-level performance. It's surreal to look back. We started the Biomni project in early 2024, when agentic AI was still nascent. It is exciting to see tens of thousands of biologists collaborating with agents every day to accelerate science. Try Biomni: biomni.phylo.bio Read more: science.org/doi/10.1126/scie… This work is not possible without this truly inter-disciplinary team: @serena2z @hcwww_ @YuanhaoQ Minta Lu, Ryan Li, @yusufroohani Lin Qiu @shiyi_c98 Gavin Junze Di @rickwierenga @kavi_deniz Sherry @TianweiShe Shruti Jennefer Xin Zhou @MWheelerMD Jon Bernstein @MengdiWang10 @PengHeAtlas @zhou_jingtian @SnyderShot @lecong Aviv Regev @jure @StanfordAILab @genentech @phylo_bio @arcinstitute @UW @berkeley_ai @RetroBio_ @tamarindbio @Princeton @UCSF

Modern multimodal models aren't a single decode loop anymore; they're composite. M* is one runtime that serves them all, and it matches or beats every specialized system: up to 2.7× on omni TTS, 12.5× on world-model rollouts. Learn more here: ai.stanford.edu/blog/mstar/

Cancer diagnosis is informed by cellular annotations of histopathology, but assays are expensive or rely on manual annotations. At #ICML2026, we present SpatialWhisperer, a trimodal model that zero-shot annotates cell types in histopathology images. 🧵👇

🎉🎉 PluRel will be presented at #ICML2026 🎉🎉 Date: Tue, Jul 7, 2026 • 10:30 AM – 12:15 PM KST Location: HALL A #2715 by co-author: @_rishabhranjan_ ! Also checkout our latest website for interactive visualizations, access to code, models and data: star-project.stanford.edu/pl…

Excited to share a research collaboration with @ScaleAILabs - we rigorously evaluate bio agents on different models across 82 drug discovery tasks - interesting findings include: (1) know-how/environment >>> models (2) different LLMs have different strength - highlighting a need for model-routing for biology agents:

Biology doesn't happen in one place. We're bringing Biomni Lab from your browser to your phone, your desktop, and your agent of choice via MCP. Biomni comes with you wherever your work happens. Sign up for the closed beta (Mobile and Desktop): forms.gle/JbB4vV4GdaZcaLU19 Use Biomni MCP today: mcp.phylo.bio/mcp Blog: phylo.bio/blog/biomni-everyw…

Can reasoning models become overly reliant on chain-of-thought examples? 🤔 Our #ACL2026 work shows excessive CoT supervision is not always beneficial, and gives a recipe for tuning the CoT fraction to improve novel-task accuracy. 🧵 Website: kvignesh1420.github.io/cot-i…

A fascinating reality check for AI coding agents. The new NanoGPT-Bench reveals that current agents (e.g., Claude Code and Codex) only recover 9.3% of human progress on AI R&D tasks.

Proteo-R1 (ICML 2026), the first reasoning protein foundation model for protein design, is out! 🚀🧬 Most protein design models generate structures without ever *reasoning* about which residues matter. We think that's backwards. Human protein engineers👩‍🔧 don't work this way. They identify critical interaction residues first — charged anchors, hydrophobic hotspots, specificity-determining motifs — and only then optimize geometry around those decisions. ━━━━━━━━━━━━━━━━ 🔬 THE CORE IDEA ━━━━━━━━━━━━━━━━ A dual-expert architecture that explicitly decouples molecular understanding from geometric generation: → ⚡A multimodal LLM (understanding expert) analyzes protein sequences, structures, and text to identify key functional residues governing binding and specificity → ⚡A diffusion model (generation expert) then co-designs sequence + structure — but with those residues locked in as hard constraints ━━━━━━━━━━━━━━━━ 📐 HOW IT'S TRAINED ━━━━━━━━━━━━━━━━ Three-stage curriculum: ① Multimodal Alignment — freeze the LLM, train projections to bridge ESM-2 + AF3-style structural features into language space ② Structural Reasoning Mid-Training — unfreeze the LLM, teach it residue grounding → pairwise geometry → interface localization → hotspot prediction ③ Joint Reasoning-Guided Design — end-to-end on antibody-antigen complexes. Gradients from the diffusion objective flow back through the reasoning expert. ━━━━━━━━━━━━━━━━ 📊 RESULTS ━━━━━━━━━━━━━━━━ Evaluated on simultaneous multi-CDR redesign and the RAbD CDR-H3 benchmark: ✅ Best RMSD & DockQ on RAbD — redesigned H3 loops are geometrically accurate *and* docked well ✅ Lowest backbone dihedral divergence (JSDbb) among all baselines ✅ Reduced intra- and inter-chain steric clashes ✅ Generated sequences score lower perplexity than native antibodies under IgLM & AbLang ✅ Plug-and-play: swapping the diffusion backend to UniMoMo still improves RMSD and IMP ━━━━━━━━━━━━━━━━ 💡 WHY IT MATTERS ━━━━━━━━━━━━━━━━ Proteo-R1 isn't just a better antibody design model. It's a blueprint for coupling deliberative LLM reasoning with any physical generative process — interpretable, modular, and backend-agnostic. 📄 Paper: arxiv.org/abs/2605.02937 💻 Code: github.com/smiles724/Proteo-… 🌐 Demo: smiles724.github.io/r1/ Great thanks to my wonderful collaborators Weihao Xuan, Heli Qi, @Hanqun_CAO, Heng-Jui Chang, @KKuanPang @XiangruTang Zehong Wang, @hcwww_ , @KejunYing @lupantech Chiho Im, Seungju Han, @richardxp888 @tikgiau. Also appreciate the guidance from advisors @YejinChoinka @jure @erranlli Naoto Yokoya, Masashi Sugiyama.

We’re working with world-class experts to encode the latest research techniques and best practices into reusable skills that any scientist can use. First up: rare-variant gene burden analysis, demonstrated on UK Biobank data for obesity. Read the case study here:

What if building production-ready predictive models was as simple as asking a question in plain English? Today, we’re launching Kumo Coding Agent Skills, an open-source library that turns coding agents like Claude Code and OpenAI Codex into experts at building advanced predictive models with the Kumo SDK. kumo.ai/company/news/introdu…