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...
My first impression is that it will look like GANs for inverse problems but maybe there is something to do with the training drift term
imo the original paper (arxiv.org/abs/2602.04770) is well written, but there are already some implementations/blog posts about it (github.com/Algomancer/M...)
🔳 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...
Is it a vscode plugin?
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...
Yes estimating distance between distributions with single sample sounds irrelevant. I wonder if flow-based artefacts are sufficiently similar across models with the same FID, allowing us to learn the score predictive model. I may try later!
Agree! I wonder if some generation artefacts are signatures that allow to predict the FID score (suppose that they are present in almost all generated images by a given model)
You may add the real test (or training 👀) dataset if you are into leaderboard chasing
