Excited to share that Assured Robot Intelligence (ARI) has joined @Meta to help build the future of humanoid intelligence! When we started ARI one year ago, our mission was clear: achieve physical AGI. Through deep customer engagements and real-world deployments, it became clear to us that serving the massive opportunity ahead requires training a truly general-purpose physical agent. We believe this agent will be humanoid — and that scaling will come from learning directly from human experience, not teleoperation alone. Meta’s ecosystem brings together the key components needed to make this vision possible. We will be joining Meta Superintelligence Labs (MSL) to help bring personal superintelligence into the physical world. We are incredibly grateful to the brilliant minds, robotics researchers, engineers, partners, and supporters who have worked with us on this journey. Thank you to our investors and angels, led by @aixventureshq , for believing in our mission. This is just the beginning.

Excited to share that ARI (Assured Robot Intelligence) is joining @Meta! When we co-founded ARI a year ago, the mission was clear: build humanoid intelligence for the real world. Joining Meta Superintelligence Labs (MSL), we'll continue advancing frontier robotics models toward physical superintelligence in the physical world. Huge thanks to my co-founders, the incredible ARI team, and our investors led by @aixventureshq for backing this from day one. This is just the beginning.

welcome Assured Robot Intelligence (ARI) to MSL! excited to build physical with @LerrelPinto @xiaolonw and the whole team!

ARI is joining @Meta! Over the past year, we have been building ARI (Assured Robot Intelligence) with the mission to build industry-grade physical AI for humanoids. The ARI stack is built on human experience, condensed into actionable tokens that can be rapidly adapted to real-world hardware. But the most rewarding part of ARI has been the people. I feel truly blessed to have worked alongside some of the world's best roboticists, a top-notch investor pool led by @aixventureshq, and the many supporters pushing for us behind the scenes. Starting next week, ARI will join the Meta Superintelligence Labs (MSL) to continue advancing frontier robotics models that advance personal superintelligence in the physical world. We have the potential to transform AI that can think and talk to AI that can do, assisting humans safely and reliably in the physical world. To the many people behind the scenes who supported us: Thank you! This is just the beginning. More in the Bloomberg article:

Excited to share that I've been selected as a 2026 Apple Scholar in AIML🎉 Huge thanks to my advisor @xiaolonw , and to all my mentors and collaborators for their support. Grateful to @Apple for recognizing and supporting my research🍎 machinelearning.apple.com/up…

🧐 We introduce TIPS: turn-level information reward shaping for tool-using LLMs. RL for agentic tasks suffers from high-variance credit assignment, especially in long-horizon tool-use settings. 👉TIPS assigns turn-level rewards based on how much each turn increases the likelihood of the correct answer — with no reward model, no human process labels, and policy-invariant shaping via PBRS. Across 8 QA benchmarks, TIPS outperforms PPO and other reward-shaping baselines, with strong gains on multi-hop and out-of-domain QA. More details in our ICLR 2026 paper: Arxiv: arxiv.org/abs/2603.22293

We scale from one institution to multiple institutions, and eventually, the whole world will be collecting data.

Our Latent Encoder-Decoder code base is fully open sourced, you can train and visualize the latent space: Code⚙️: github.com/EmptyBlueBox/DexL… ArXiv 📚: arxiv.org/abs/2603.10158 #CVPR2026

One policy that rules all the hands.

What an exciting time for building.

Why buy a robot when you can build your own? Meet YOR, our new open-source bimanual mobile manipulator robot – built for researchers and hackers alike for only ~$10k. 🧵👇

The paper is now available: huggingface.co/papers/2602.0… More updates coming soon!

Learning world models from touch instead of pixels. Led by @cwj99770123 and @yswhynot

Can we bridge the Sim-to-Real gap in complex manipulation without explicit system ID? 🤖 Presenting Contact-Aware Neural Dynamics — a diffusion-based framework that grounds simulation with real-world touch. Implicit Alignment: No tedious parameter tuning. Tactile-Driven: Captures non-smooth contact events. Consistent: Stable predictions in contact-rich tasks.

1/ General-purpose robotics is the rare technological frontier where the US / China started at roughly the same time and there's no clear winner yet. To better understand the landscape, @zoeytang_1007, @intelchentwo, @vishnuman0 and I spent the last ~8 weeks creating a deep dive on humanoid robotics hardware and flew to China to see the supply chain firsthand. Here's everything we've created our takeaways about the components, humanoid comparisons, supply chains, and geopolitics👇

Standard AI learns to imitate. We introduce a new framework that trains AI to make new discoveries in science engineering. Learning-to-discover open source LM led to: 🥇best new bound on Erdos min overlap problem 🥇fastest GPU kernels 🥇better single-cell denoising more!

TTT now beats AlphaEvolve in math problems, with a few hundred dollars and an open model. With Test-Time Training (TTT) RL, the model continues learning on the job from its own attempts.

We are entering a new era for LLM memory. 🧠 In our latest research, End-to-End Test-Time Training, LLMs keep learning at test time via next-token prediction on the context – compressing what they read directly into their weights. Learn more: nvda.ws/4sHb8na

After years of research, there is finally a solution to long context! TTT-E2E is how robotics will work in the future. Humanoids ingest vision, touch, audio – almost everything humans do. It only makes sense if robot memory works like human memory: learning during deployment.