Berkeley AI Research @berkeley_ai
We're graduate students, postdocs, faculty and scientists at the cutting edge of artificial intelligence research. bair.berkeley.edu Berkeley, CA Joined July 2017-
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"Video models are world simulators." Ok — can they predict a ball bouncing off another ball, off another ball? 🤨 We find standard video diffusion breaks down as the chain of dependent events lengthens — and more compute isn't the fix. Meet the seriality gap. 🧵 1/n
My piece in this impressive and fascinating compilation of distinguished social scientists, artists and humanists thinking about AI. Stone soup, the Golem and and the intelligences of exploration and care. humanistreview.ai humanistreview.ai
My MIT commencement speech now online. I worked very hard on this, trying to put together the MIT scientists, children, human evolution,caregiving grandmoms and the Lunar Society - I hope it provides some hope and inspiration in a hard time for science. youtube.com/watch?v=5YNs-Y…
We got 99% on ARC-AGI-3 by instilling analysis-by-synthesis into AI models. [Schema] is basically VIGA squared: do inverse graphics, then do it again one level up — inverse dynamics. Super simple, surprisingly effective! Analysis by synthesis FTW!!!
Today, we’re introducing [schema]: a harness reaching 99% RHAE with Opus 4.8 + Fable 5 and 95.35% with GPT-5.6 Sol on ARC-AGI-3 Public set. [schema] makes an LLM think like a physicist. 🧵
Today, we’re introducing [schema]: a harness reaching 99% RHAE with Opus 4.8 + Fable 5 and 95.35% with GPT-5.6 Sol on ARC-AGI-3 Public set. [schema] makes an LLM think like a physicist. 🧵
Existing VLMs can be very useful for robotics but they struggle to answer multiple-choice questions about robot scenes. This paper explores this gap and provides benchmarks for improvement. Kudos to the team of authors led by postdocs Shuangyu Xie and Eric Chen!
🤖How well do today's VLMs actually understand real-world robotics? 👀 Excited to share RoboVista at #RSS2026 — a systematic evaluation and benchmark for VLMs across diverse, real-world robot applications. Website, dataset and paper: berkeleyautomation.github.io/robovista/ Developed by
🤖How well do today's VLMs actually understand real-world robotics? 👀 Excited to share RoboVista at #RSS2026 — a systematic evaluation and benchmark for VLMs across diverse, real-world robot applications. Website, dataset and paper: berkeleyautomation.github.io/robovista/ Developed by researchers at @UCBerkeley, @GoogleDeepMind, and @Princeton. 🧵👇
Novel approach to training tactile policies..
This was my final Ph.D. work co-led by Rosy Chen and I. Prior work fixates on zero-shot sim-to-real resulting in compromises such as the choice of simple sensors like proprioception. With PTLD we show that if you're allowed to collect a little real data, you can actually deploy
Intelligence sufficient for knowledge work is rapidly becoming free. What does this mean for the future of data systems and agents? A new blog post outlines a new research agenda @Berkeley_EECS; 🧵 bair.berkeley.edu/blog/2026/07/0…
Released on @berkeley_ai blog, a perspective from @adityagp & collaborators. As agents take on more knowledge work, what happens to the data systems underneath? A look at systems for, of, and by agents … 🐻📄 bair.berkeley.edu/blog/2026/07/0…
Very excited about GaP’s potential to increase throughput and support near-term robotics applications. Hats off to team led by brilliant postdocs Eric Chen and Shuangyu Xie and superb colleagues from @BoschGlobal and @NVIDIARobotics.
Graph-as-Policy (GaP) is a new variant of Agentic Robotics from @nvidia and @UCBerkeley that builds computation graphs (like ROS) to ensure modularity, manage complexity, and facilitate interpretability. 🧵Open code and paper: graph-robots.github.io/gap/ #Robotics #Automation
Graph-as-Policy (GaP) is a new variant of Agentic Robotics from @NVIDIA and @UCBerkeley that builds computation graphs (like ROS) to ensure modularity, manage complexity, and facilitate interpretability. 🧵Open code and paper: graph-robots.github.io/gap/ #Robotics #Automation #AgenticAI #EmbodiedAI
Excited about WARP-RM: by modulating the time-sampling on a few high-quality human demonstrations, we train a progress measure to extract high-quality segments from all demos, yielding significant improvement in folding throughput. Hats off to Justin and the team for this idea!
Introducing WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation 🧵 We gave our t-shirt folding robot more demonstrations and it got worse. Every extra demo ended in a successfully folded shirt. The data wasn't bad. It was noisy. The policy couldn't tell
postering tomorrow hall A 2:30pm #3110 (& 5pm #3111) YES it looks like an instagram infographic NO I don't care we have bigger fish to fry
we analyzed >100k posts from r/ChatGPT over 3 years on one hand, we saw ChatGPT quickly become normalized as an everyday consumer product, which is pretty cool on the other hand…
I am thrilled to share that UC Berkeley and UCSF have launched a joint initiative in Computational Biomedicine! cdss.berkeley.edu/news/uc-berkel… We will soon be recruiting new faculty and postdoctoral fellows. Please repost to help spread the word.
Everyone is talking about self-evolving AI, or recursive self-improvement. The methods that built modern ML are the ones that keep working across settings and scales, yet no benchmark directly tests AI systems for that ability. Today, after months of cross-platform validation with external teams, we're proud to introduce MLS-Bench, a benchmark for ML Science with 140 tasks across 12 ML domains. It asks whether AI systems can create scalable and generalizable ML methods, the way human researchers have pushed AI forward. (1/9)
Did a model secretly train on another model's outputs? Providers can catch this from server-side logs — Anthropic recently reported catching 29M such exchanges. But those methods need usage logs and infrastructure access; they don't work from the model itself. Our paper does: compare a model to an earlier checkpoint of itself, and ask which candidate teacher best explains the shift between them. The true teacher stands out. 🧵
If you want a robot to do something well, you need to know how to talk to it. If you don't, you can learn, with Semantic Action RL! In our paper, @JagdeepBhatia8, @ajwagenmaker, @verityw_ show how RL over VLA prompts enables new tasks and learns blazing fast in the real world!
How can generalist policies adapt to new challenges at deployment using skills they already have? We optimize VLA *prompt inputs* with reinforcement learning, enabling efficient real-robot adaptation on complex tasks where existing methods struggle. 🧵 semantic-action-rl.github.io
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