🎆 New paper! "Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields", by Julien Lalanne, accepted to ICML'26 🥳 We're proposing flow-matching for inpainting in ultra-sparse setup, with applications to seismic interpolation. 📜 arxiv.org/abs/2605.28625 1/

"Accept (spotlight)" at ICML'26 😎 Our paper brings particle filters back to life: autoregressive diffusion models + posterior sampling yield optimal proposals for Bayesian filtering, scaling up to GenCast-sized systems. arxiv.org/abs/2605.20028 w/ Thomas Savary and @francois-rozet.bsky.social

Thrilled to share that MIRO is accepted to ICML 2026 @icmlconf.bsky.social ! 🎉 By training on the reward scores, we can simply condition the model on high rewards at inference time to guarantee top-tier, aligned outputs. We’ve updated our paper with some additional results!

📢 The TerraBytes workshop is returning for a 2nd edition - this time at ECCV 2026. Submit your paper before June 18th and join in Malmö, Sweden! 🔗 terrabytes-workshop.github.io

👏 Folks! If you are curious about the Generative Modeling via Drifting paper, but you find it difficult to understand → I wrote a different interpretation of it. It's called: "An Expectation-Maximization interpretation of Generative Modeling via Drifting" davidpicard.github.io/pdf/An_Expec...

My PhD thesis manuscript will be available in the coming months, but I’ve written two blog posts based on the related work chapter: 1. Generative modeling with flow-based models 🪚 2. Data-translation with flow and diffusion bridges 🔨 Open to feedback and discussions! lebellig.github.io/blog/

Want better local models? Want models that are made completely transparently? OpenAthena.ai is hiring on our Marin.community team. We’re especially looking for SWEs and research scientists with experience in data and post training. DM me.

My first blog post in over a year is a deep dive on flow maps🗺️, or how to learn the integral of a diffusion model to enable faster sampling and several other cool tricks. It's the longest one yet👀 Let me know what you think! sander.ai/2026/05/06/f...

My PhD thesis manuscript will be available in the coming months, but I’ve written two blog posts based on the related work chapter: 1. Generative modeling with flow-based models 🪚 2. Data-translation with flow and diffusion bridges 🔨 Open to feedback and discussions! lebellig.github.io/blog/

Delighted to have successfully defended my PhD thesis on "Generative models for Earth Observation, from denoising to domain adaptation" 🪴 This wouldn’t have been possible without the support of my colleagues, my PhD advisor, and my family and friends. Thank you all!

While it is intuitively clear that straighter trajectories should reduce discretization error when integrating an ODE (for instance, in flow matching), I could not find a precise bound. I therefore rewrote the proof of Cauchy-Lipschitz to make this explicit. github.com/gpeyre/Discr...

I wrote a short mathematical companion tutorial to my notebook on discrete diffusion models. It gives an informal derivation of the connection between maximum likelihood estimation of the backward transition kernel and denoising score matching. github.com/gpeyre/Discr...

What if AI could invent enzymes that nature hasn’t seen? 👩‍🔬🧑‍🔬 Introducing 🪩 DISCO: Diffusion for Sequence-structure CO-design 📝 Blog: disco-design.github.io 📄 Paper: arxiv.org/abs/2604.05181 💻 Code: github.com/DISCO-design...

Two research blog posts that look really interesting 👀 "Teaching AI to Invent Enzymes Nature Never Imagined", DISCO: Diffusion for Sequence-structure CO-design disco-design.github.io "How to Generate Text in One Step", Flow Map Language Models one-step-lm.github.io/blog/

🚨 arxiv.org/abs/2604.06129 PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer This paper is the result of doing a lab-wide hackathon on an idea I've had for some time. Probably the paper with the highest number of authors I've ever done. It's a CVPR Findings 26. Thread 🧵👇

For those interested in normalized gradient methods and optimal transport: I introduce a new class of "spectral" Wasserstein distances for which spectrally normalized gradient descent (Muon but without momentum and small step size ...) is a spectral-W gradient flow: arxiv.org/abs/2604.04891

We're excited to provide more information about our upcoming annual workshop - The 2026 Nordic Workshop on AI for Climate (climateainordics.com/events/2026-...) - to be held on June 26th, 2026 at University of Copenhagen! Registration link coming very soon!

🔮 Working on ML on curved manifolds? Don't miss out on Jacobi Fields! 🔮 I wrote a quick, highly visual and hopefully accessible introduction to the topic: "Jacobi Fields in Machine Learning" 🤠 Check it out here: olgatticus.github.io/blog/jacobi-...!

Today NeurIPS is announcing our official satellite event in Paris. After responding to the call from Ellis following the success of EurIPS in December, we are pleased to reach a new milestone by joining forces with the NeurIPS organizing committee for the 2026 edition.

Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis by Hila Chefer et al. (arxiv.org/abs/2603.06507). New SSL loss for flow matching that encourages meaningful representation learning without relying on an external visual encoder for alignment. Improves generation on many modalities.

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

In October, I gave a talk at ML in PL in Warsaw: a whirlwind tour of what goes into training image and video generation models at scale. 📺 video: www.youtube.com/watch?v=qFIT... 🖼️ slides: docs.google.com/presentation...

📢 Je recrute : ingé ou postdoc (12 mois) ➡️ www.ign.fr/nous-rejoind... Venez entraîner des grands modèles génératifs pour le bien commun : 🗺️ données ouvertes (images aériennes/satellites) 🏞️ application au suivi du changement climatique et à la gestion des catastrophes naturelles #lastig #ign

The Spacetime of diffusion models: an information geometry perspective by Rafał Karczewski et al. (arxiv.org/abs/2505.17517) blog: rafalkarczewski.github.io/blog/2026/di... Geodesics in the (xt, t) spacetime of diffusion models -> new distance between clean data points + transition path sampling!!

I have added a new tutorial on discrete diffusion models: github.com/gpeyre/ot4ml

I had a draft about how wild the pace of new generative models was… written two months ago. It’s already outdated. Somehow, things are moving even faster now... (and yes I’m back to posting about generative models)

Too many REPA / RAE / representation alignment papers lately? I was lost too, so I wrote a blog post that organizes the space into phases and zooms in on what actually matters for general/molecular ML. Curious what folks think - link below! 🔗 Blog: kdidi.netlify.app/blog/ml/2025...

🔳 Discrete drifting models 🔳 Riemannian drifting models 🔳 Optimal Transport drifting models 🔳 Image2image drifting models 🔳 Time-dependent drifting models (tricky one) 🔳 Adversarial drifting models 🔳 Wasserstein drifting models 🔳 Variational drifting models 🔳 Functional drifting models

Very cool PhD project on generative models for dense detection of rare events in Earth Observation 🌍🌱 Nicolas has been my supervisor for the last 3 years, highly recommend doing a PhD with him!

📢 Fully funded PhD - 🌍 Dense Detection of Rare Events in Remote Sensing using Generative Models Leverage generative models, unsupervised segmentation and explainability techniques to map disasters w/ @javi-castillo.bsky.social and Flora Weissgerber Apply ⤵️ recrutement.cnes.fr/fr/annonce/4...

Meta Flow Maps enable scalable reward alignment, Peter Potaptchik et al. (arxiv.org/abs/2601.14430) This article introduces Meta Flow Maps: a stochastic generalization of consistency models (one-step generation) that allows efficient reward steering at inference time or during fine-tuning.

I'm excited to open the new year by sharing a new perspective paper. I give a informal outline of MD and how it can interact with Generative AI. Then, I discuss how far the field has come since the seminal contributions, such as Boltzmann Generators, and what is still missing

Should we ban Brian Eno from bandcamp?

New blog 💙: I reflect on why I worked on what I worked on... I think a PhD is a very special time. You get to challenge yourself, push your boundaries, and grow. My thoughts go against the current AI/academia narrative online, so I hope you find it interesting. chaitjo.substack.com/p/phd-thesis...

1. Select many diffusion/flow-matching models 2. Generate 50k images per model 3. Use FID of each set as a label 4. Train a model to predict FID from a single image What’s the probability this actually works, gives a cheap proxy for FID and enable fast generative model prototyping?

We introduce epiplexity, a new measure of information that provides a foundation for how to select, generate, or transform data for learning systems. We have been working on this for almost 2 years, and I cannot contain my excitement! arxiv.org/abs/2601.03220 1/7

📖 We put together with Mike Davies a review of self-supervised learning for inverse problems, covering the main approaches in the literature with a unified notation and analysis. arxiv.org/abs/2601.03244

Can we train neural networks just with permutations of their initial weights? And then whats the best initialisation distribution ?

PS: We also recently released a unified codebase for discrete diffusion, check it out! 𝕏 Thread : x.com/nkalyanv99/... 🔗 GitHub: github.com/nkalyanv99/... 📚 Docs: nkalyanv99.github.io/UNI-D2/

🆕 “Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction” Huge thanks to Tobias Hoppe, @k-neklyudov.bsky.social, @alextong.bsky.social, Stefan Bauer and @andreadittadi.bsky.social for their supervision! 🙌 arxiv : arxiv.org/abs/2512.05092 🧵👇

"Improved Mean Flows: On the Challenges of Fastforward Generative Models" arxiv.org/abs/2512.02012 questions this approximation and proposes a new training process for mean flows.

Hi @ google, can you provide 100k TPU hours to explore the design space of diffusion bridges for image-to-image translation? x1 vs drift pred, architectures and # params, # dataset, scaling couplings and batch sizes (for minibatch-based couplings). I can run everything in jax in return...

Yesterday, @nicolasdufour.bsky.social defended is PhD. I really enjoyed the years of collaboration w/ @vickykalogeiton.bsky.social (& @loicland.bsky.social) Video: youtube.com/live/DXQ7FZA... Big thanks to the jury @dlarlus.bsky.social @ptrkprz.bsky.social @gtolias.bsky.social A. Efros & T. Karras

@climateainordics.com is now on youtube! Check out some amazing talks on how to help fight climate change using AI! youtube.com/@climateaino...

@neuripsconf.bsky.social is two weeks away! 📢 Stop missing great workshop speakers just because the workshop wasn’t on your radar. Browse them all in one place: robinhesse.github.io/workshop_spe... (also available for @euripsconf.bsky.social) #NeurIPS #EurIPS

Calling it for today... I tried using the gemini 3 Pro preview to build some js animations, and it went well

Interpolation between two gaussian distributions on a flat torus (my personal benchmark for new llms)

It may not top ImageNet benchmarks, but honestly, that hardly matters... the removal of the VAE component is a huge relief and makes it much easier to apply diffusion models to domain-specific datasets that lack large-scale VAEs.

"Back to Basics: Let Denoising Generative Models Denoise" by Tianhong Li & Kaiming He arxiv.org/abs/2511.13720 Diffusion models in pixel-space, without VAE, with clean image prediction = nice generation results. Not a new framework but a nice exploration of the design space of the diffusion models.

We figured out flow matching over states that change dimension. With "Branching Flows", the model decides how big things must be! This works wherever flow matching works, with discrete, continuous, and manifold states. We think this will unlock some genuinely new capabilities.