Raúl

PhD @ AITHYRA | AI x Bio

[Kevin K. Yang 楊凱筌 @KevinKaichuang]

A model that generates an enzyme's reaction mechanism from its sequence, catalytic residues, substrates, and cofactors.

@arianemora_

[Romeo Passaro @rom_pas_]

Happy to share our new work: Distributional Functional Rewards (DFR)! 🎉

We improve diversity and fidelity in diffusion fine-tuning 🔧✨

Big thanks to my coauthors @JeongjaeLee0, Jong Chul Ye, @mmbronstein, @Yoshua_Bengio, @AlexanderTong7 and @danyalrehman17! 🙏 Thread below 👇

[Zander Blasingame @zwblasingame]

Excited to announce the follow-up to our NeurIPS 2026 Oral: Improved Training of Strong Stochastic Flow Maps!

We improve the training of SSFMs by unlocking derivative-based losses!

Joint work with @antoniofrancaib @rmincam @ChristianKoke @Niklas_TR and @AlexanderTong7

[Joey Bose @bose_joey]

🚨 New Paper Alert, 🎤 Accepted for an oral presentation at the NeurIPS 2026 STODY workshop!

📣 Introducing: Normalizing Flow Maps (NFM): Combining few-step transport from Flow maps with tractable likelihoods from Normalizing Flows.

This is my first work with my first crop of new PhD students, @francescoshox and @tim_hitge, who genuinely knocked this project out of the park.

📄 Paper: https://t.co/LCFBg1QjLT
📝 Blog: https://t.co/8dWEYwUEOm
💻 Code: https://t.co/Oroppp6NBR

🧵
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[Niklas Rindtorff @Niklas_TR]

Introducing Langevin Flow Maps

TLDR: Neural Network Potentials that learn their own stochastic pathwise solution for faster simulation.

- Transferable across molecular systems.
- Transition dynamics are preserved.
- No trajectory data are required.

Work together with @sgmccallum, supervised by @AlexanderTong7, and @JamesFosterBath.

Arxiv: https://t.co/GGgujg56YH
Data: https://t.co/TeYvKN13o7
Code: https://t.co/HYiMqTI4jT

[Katarina Petrovic @kpetrovvic]

New preprint: 🚀 Global Transport (GT) couplings for classifier-free guided flows!
Surprisingly, a simple label-free optimal transport coupling beats both class-conditional OT and independent couplings once we push the CFG scale up 🧗 reaching 1.91 FID on ImageNet-256

[Alex Tong @AlexanderTong7]

Happy to announce I've been named a 2026 #AI2050 Early Career Fellow by @SchmidtSciences. I’ll investigate generative models for molecules as part of a global cohort working to ensure AI benefits society by 2050.

[Jack Morris @jxmnop]

"No man ever runs an experiment on the same infra twice, for it's not the same infra and he's not the same man." - Heraclitus

[Zander Blasingame @zwblasingame]

Strong Stochastic Flow Maps has been accepted to #NeurIPS2026 as an Oral! 🎉

[Adrian Dobbelstein @AdrDobbel]

Happy to share our preprint: "A Unified 3D Generative Model for Synthesizable Structure-Based Drug Design". Highlights, preprint, and code below: 🧵1/10

[Ayhan Suleymanzade @ayhozade]

Can neural networks learn to think by denoising?

Excited to share Thinking with Looped Flows!

We train recurrent reasoning with local denoising objectives. It is a new approach for reasoning that achieves SoTA on ARC-AGI-1 and 2 among looped models.

In collaboration with @chandavidlee, @FEijkelboom, @nmboffi, @ismaililkanc, @jw9730.

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[Alex Tong @AlexanderTong7]

We're recruiting PhD students at #AITHYRA in Vienna! Working on ML methods at the intersection of AI and biology.

Details & apply: https://t.co/UzneeVBnxl

Feel free to reach out if you have questions!

[Sander Dieleman @sedielem]

New blog post: continuous diffusion for language is back!

This research direction receded into the background for a while, but as of this year, it is once again a hot topic. I wrote down a historical perspective and some thoughts on the recent revival.

https://t.co/wNafED6aDB

[Ayaan Zaidi @obviyus]

"We sandboxed the agent"

The agent:

[Raúl @rmincam]

@ValentinDeBort1 @marikgoldstein

[Alexis Molina @alexismolmr]

New preprint: Do more complex single cell perturbation models generalize better to unseen perturbations and cellular contexts? Not necessarily.

We find that generalization depends more on aligning model inputs with the response being predicted.

https://t.co/3JbwgSj0fo

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[Austin Cheng @auhcheng]

💠Introducing Clari, a generative model to predict organic crystal structure in seconds.

Clari’s speed makes it feasible to do virtual screening of organic molecules on the basis of solid-state properties.

co-first with Alston Lo @alston1o and Luka Mucko @srboljubbosanac

[Zander Blasingame @zwblasingame]

🚀Strong Stochastic Flow Maps at @icmlconf tomorrow!

Drop by our SPIGM workshop poster:
📌 Poster in Hall A | 11:40-13:30

Joint work with @sgmccallum and the rest of the amazing team: Timothy Herschell, @Niklas_TR, @AlexanderTong7, @JamesFosterBath

[Antonio Franca @antoniofrancaib]

Tomorrow, I'm at #ICML2026 presenting our paper at the Structured Probabilistic Inference & Generative Modeling (SPIGM) workshop:

📄 "Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics"

w/ @AlexanderTong7

🧵👇

[Christian Koke @ChristianKoke]

🚨GNNs are not continous across Graph Resolutions🚨
#ICML2026

🧵1/5 Should GNNs produce similar representations for the same object at different resolutions?

A social network of individual users vs one of interacting groups.

A molecule with all atoms vs just heavy atoms.