We present an Autoregressive U-Net that incorporates tokenization inside the model, pooling raw bytes into words then word-groups. AU-Net focuses most of its compute on building latent vectors that correspond to larger units of meaning. Joint work with Badr Youbi Idrissi 1/8

Here is the recording with the slides for those interested! 🎤 youtu.be/UONvP1TL0-g?fe… 📊drive.google.com/file/d/14ZIopS… 📑arxiv.org/pdf/2410.02724 Cohere Labs Cohere Labs

❓How to balance negative and positive rewards in off-policy RL❓ In Asymmetric REINFORCE for off-Policy RL, we show that giving less weight to negative rewards is enough to stabilize off-policy RL training for LLMs! 💪 (1/8) Paper: arxiv.org/abs/2506.20520

Our research on embodied AI agents that can perceive, learn, act and interact in the virtual and physical worlds. #metaAI #AIAgent #embodied #worldmodel #superintelligemce arxiv.org/abs/2506.22355

Anthropic or Anthropic-sponsored safety papers

Why does Meta open-source its models? I talked about it with Maciej Kawecki - This Is IT looking at Dino, our computer vision model with applications in forest mapping, medical research, agriculture and more. Open-source boosts AI access, transparency, and safety. youtube.com/watch?v=eNGafi…

heading to ICML Conference #ICML2025 next week! come say hi & i'd love to learn about your work :) i'll present this paper (arxiv.org/abs/2503.17514) on the pitfalls of training set inclusion in LLMs, Thursday 11am here are my talk slides to flip through: ai.stanford.edu/~kzliu/files/m…

🚀 We are happy to organize the BERT²S workshop NeurIPS Conference 2025 on Recent Advances in Time Series Foundation Models. 🌐 berts-workshop.github.io 📜Submit by August 22 🎓Speakers and panelists: Chenghao Liu Mingsheng Long Zoe Piran Danielle Maddix Ameet Talwalkar Qingsong Wen, PhD, Head of AI @ Squirrel AI

If you are attending ACL2025 join our oral presentation! Happening at 15:00 in room 1.86 🙂

🚀New paper alert! 🚀 In our work AI at Meta we dive into the struggles of mixing languages in largely multilingual Transformer encoders and use the analysis as a tool to better design multilingual models to obtain optimal performance. 📄: arxiv.org/abs/2508.02256 🧵(1/n)

Peer review ML conferences: > Only "top" X% get in > Jobs, grants, bonuses hinge on it > No penalty for bad-faith reviews > No cost for flooding submissions Who could have seen this going wrong? 🤭

Say hello to DINOv3 🦖🦖🦖 A major release that raises the bar of self-supervised vision foundation models. With stunning high-resolution dense features, it’s a game-changer for vision tasks! We scaled model size and training data, but here's what makes it special 👇

I am happy to share the work of our team. The outcome of a collaborative effort, by a joyful group of skilled and determined scientists and engineers! Congrats to the team on this amazing milestone!