Please help spread the word! We’re hiring Research Scientists and Research Interns at NVIDIA Research to work on some of the most exciting problems in generative AI: 🌎 World Models & Video Diffusion 🧠 Diffusion Language Models 🧬 Biomolecule Design If your research aligns with any of these areas, please apply directly through the links below. Open positions + application links: research.nvidia.com/labs/gen…

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-W…

In the future all work becomes play

New paper! Presenting Discrete Flow Maps: paper: arxiv.org/abs/2604.09784 blog: malbergo.me/discrete-flow-ma… A laughable problem for me these days is that @nmboffi and I share a research brain, and we have had, time and again, a conversation that ends with “ha so I guess we’re writing the same paper.” Soon we will return to just doing it together :). Here we are doing it again with discrete flow maps and flow language models! A complete and thorough paper led by @PPotaptchik @json_yim @adhisarav @peholderrieth. We took a bit of time to post it to ensure we understood a few more things about the stability of the loss functions. Like @osclsd , @FEijkelboom, and @nmboffi , we think this could be a very helpful paradigm for thinking about fast inference and even better alignment! Here’s our version of the story, and I hope it makes clear how green field this research direction is — we provide a comprehensive picture of the KL losses you can write from the properties of the flow map, some nice geometric proofs about the mean denoiser and the simplex, and find that at this time, the ESD can actually be the most performant, with some caveats. Excited for everyone to work together and push this class of models to their limit!

1/ Deep learning is going to have a scientific theory. We can see the pieces starting to come together, and it's looking a lot like physics! We're releasing a paper pulling together these emerging threads and giving them a name: learning mechanics. 🔨 arxiv.org/pdf/2604.21691 🔧

debugging Codex sessions with others was oddly painful “can you copy paste that?” “wait send the latest version with tool calls expanded” so I built CodexLink ask codex to share → get a link → stays in sync way better for working on CLIs for agents + agent skills try it:

Most Dense Associative Memory models do this: 👉 1 hidden neuron activates 1 memory That’s a bottleneck. Real-world data is compositional — thus our models should be too. @mohi_shafiei designed a model that has this desired property! The configurations of visible neurons are encoded by combinatorial barcodes of activations in the hidden layer. The local minima of the energy (memories) become compositional — built from a set of core features encoded by individual hidden neurons. And here’s the striking part: 👉 Memory capacity becomes exponential in the number of HIDDEN neurons. Not linear, as in previous models. Check out our new #ICLR2026 paper: arxiv.org/abs/2601.00984. What would you build with this?