Not that long ago, building a machine learning system meant carefully designing features, choosing a model architecture, training it from scratch, tuning hyperparameters, and repeating this process for every new problem. Then foundation models changed how we think about text, images, and increasingly other modalities: pretrain once, then adapt to new tasks through context. The same transition is now happening for structured data. With Kumo Tabular, you provide a table with labeled examples and rows you want predictions for. The model produces predictions in a single forward pass --— with no task-specific training, no fine-tuning, and no feature engineering. What makes this especially exciting to me is that this is not just a new model, but part of a rapidly growing research ecosystem around tabular foundation models. There is tremendous innovation happening across academia and industry in architectures, synthetic pretraining, in-context learning, evaluation, and efficient inference. NVIDIA wants to be an active part of that ecosystem —-- contributing research, releasing models openly, and building infrastructure that helps the community push the field forward. There are several aspects of the work I find particularly interesting. ** The models are pretrained entirely on synthetic tables generated from structural causal models, allowing us to expose them to enormous diversity without training on customer data or benchmark datasets. The largest model sees more than 100 million synthetic tables during pretraining. ** The resulting models are both accurate and efficient. Across major tabular benchmarks, Kumo Tabular improves upon strong existing approaches while requiring no per-dataset training. On TabArena, for example, Kumo Tabular Large sits on the accuracy–speed Pareto frontier and is 17× faster at prediction than LimiX-2. To me, the bigger story is the direction ML is moving: from hand-built models for individual tasks to pretrained models that learn broad representations of a domain and can solve new problems from context. We have seen this transformation in language and vision. It is exciting to see it now reaching the enormous world of structured data. Huge congratulations to the team — and to the broader tabular foundation model research community whose ideas and work are making this new paradigm possible. We’re excited to contribute, learn, and help build this ecosystem together.

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.