I’m in Seoul 🇰🇷 this week for #ICML2026 If you’re interested in our Brain2Qwerty work or brain decoding more broadly, I’d love to chat! Feel free to reach out. I’ll also be at the @AIatMeta booth presenting a demo of TribeV2 from our team. 🧠 See you there!

What advantage to use, and when? Everyone's proposing new advantage functions for RL with LLMs but nobody knows why they work or fail. We break this down and build FADE a self-adapting advantage to get 14% on LiveCodeBench v6 in 40% less steps. Paper: arxiv.org/pdf/2607.01490

🚨 BIG UPDATE from Brain & AI team, bringing us one step closer to decoding language from the brain: 🧠 Brain2Qwerty v1 published at Nature Neuro 🚀 Brain2Qwerty v2 released publicly A huge effort led by @JarodLevy and @LucyZ47712090 🙌 Read more: facebookresearch.github.io/b…

Forget Neuralink, A new path to communication without surgery. Meta AI’s Brain2Qwerty v2 delivers real-time sentence decoding from raw brain signals. the highest-performing non-invasive brain-to-text system. End-to-end AI LLM fine-tuning, 61% average word accuracy up to 78% for top performers, Non-invasive,Trained on 22,000 sentences getting closer to surgical-level performance through scaling, Real hope for millions who can’t speak due to brain conditions. this is wild & Open-sourced to accelerate progress for everyone. Training code and dataset now publicly available, Huge step

Highly recommended reading. What an impressive use of LLMs and deep learning. Achieves "real-time sentence decoding from non-invasive brain recordings, approaching levels of accuracy previously exclusive to techniques that require brain surgery."

This is exciting and all, but the most interesting part for me is this: Auto Research, powered by @cursor_ai agent. The AI agent independently wrote code, ran experiments, analyzed the results, and improved the model's Word Error Rate by up to 19.8%, vastly outperforming traditional hyperparameter search algorithms (Optuna). And that's because agents weren't just tweaking hyperparameters. They autonomously discovered and coded ML techniques to make the brain-decoder better. Multiple agents independently invented strategies like "modality dropout" (forcing the AI to rely more on brain signals rather than its own language predictions) and good old beam search decoding. Vibe-science era, what can i say.

some exciting new work from our AI teams at Meta on non-invasive brain computer interfaces!

This is cool! Mark, I Owe You an Apology. I Wasn't Really Familiar With Your Game.

🚀 Brain2Qwerty v1 is in Nature Neuroscience, and today we're releasing v2 😎! 🧠⌨️ Same goal, but v2 slashes the best subject Word Error Rate from 52% to 23%, avg 39%—significantly narrowing the performance gap with invasive BCIs. 📄 Meta AI Blog: ai.meta.com/blog/brain2qwert… 👇

We’re happy to announce 2 releases today: - 🧠Brain2qwerty v1 is published at @NatureNeuro - 🚀 Brain2Qwerty v2 is now publicly released Explore how we decode sentences from non-invasive brain recordings: facebookresearch.github.io/b… Links: 📄v1 Nature Neuro: nature.com/articles/s41593-0… 📑v2 Meta preprint: facebookresearch.github.io/b… 💻Code: github.com/facebookresearch/… 📊Data: huggingface.co/datasets/bcbl… 📝Blog: ai.meta.com/blog/brain2qwert… 🧵Thread: x.com/JarodLevy/status/20715…

Brain2Qwerty V2 is out 💫

We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2. Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication. We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating. 🧵👇