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Why do we keep sampling from the same distribution the model was trained on? We rethink this old paradigm by introducing Feynman-Kac Correctors (FKCs) – a flexible framework for controlling the distribution of samples at inference time in diffusion models! Without re-training

Check out FKCs! A principled flexible approach for diffusion sampling. I was surprised how well it scaled to high dimensions given its reliance on importance reweighting. Thanks to great collaborators Mila - Institut québécois d'IA Vector Institute Imperial College London and Google DeepMind. Thread👇🧵

✨Cradle is hiring protein+ML researchers!✨ We operate ML for lab-in-the-loop lead optimization across all industries (pharma, synbio, ...), modalities (antibodies, enzymes, ...), properties (binding, activity, ...) We're a scaleup and already relied upon by 4 of the top 20

Given q_t, r_t as diffusion model(s), an SDE w/drift β ∇ log q_t + α ∇ log r_t doesn’t sample the sequence of geometric avg/product/tempered marginals! To correct this, we derive an SMC scheme via PDE perspective Resampling weights are ‘free’, depend only on (exact) scores!

(1/n) Sampling from the Boltzmann density better than Molecular Dynamics (MD)? It is possible with PITA 🫓 Progressive Inference Time Annealing! A spotlight GenBio Workshop @ ICML25 of ICML Conference 2025! PITA learns from "hot," easy-to-explore molecular states 🔥 and then cleverly "cools"

🎉Personal update: I'm thrilled to announce that I'm joining Imperial College London Imperial College London as an Assistant Professor of Computing Imperial Computing starting January 2026. My future lab and I will continue to work on building better Generative Models 🤖, the hardest

Our team spent a massive amount of time to provide a review of what we call "general purpose models" for the chemical sciences. We explain fundamentals and go through applications and broader implications. arxiv.org/abs/2507.07456

1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #NeurIPS2025! 📢 FPI@NeurIPS 2025: Frontiers in Probabilistic Inference – Learning meets Sampling Learn more and submit → fpiworkshop.org

👋 I'm at #ICML2025 this week, presenting several papers throughout the week with my awesome collaborators! Please do reach out if you'd like to grab a coffee ☕️ or catch up again! Papers in 🧵below 👇:

let’s gooooo

Come check out SBG happening now! W-115 11-1:30 with charliebtan Joey Bose Chen Lin Leon Klein Michael Bronstein

we’re not kfc but come watch us cook with our feynman-kac correctors, 4:30 pm today (july 16) at ICML Conference poster session — east exhibition hall #3109 Kirill Neklyudov Alex Tong Tara Akhound Sadegh Viktor Ohanesian

When designing AI scientist benchmarks, the challenge lies in how to simulate realistic experimental data. We find that the systems biology provide a great simulator for this! I am currently at ICML. Very excited to chat with anyone interested in this work!

Check out SynCoGen-our new co-generation model for synthesizable small molecules! We define building block & reaction-level graphs which we learn via masked graph diffusion and couple this with flow matching to learn atomic coordinates in 3D space. 🧵

🚀 GenMol is now open‑sourced: you can now train and finetune on your data! It uses masked diffusion + a fragment library to craft valid SAFE molecules, from de novo design to lead optimization. #GenMol #DrugDiscovery #Biopharma

AI4Mat is back for NeurIPS! time to crystallize those ideas and make a solid-state submission by august 22, 2025 💪 new this year: opt-in your work for our Research Learning from Speaker Feedback program -- a new structured discussion format where spotlight presenters receive