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Introducing GEN-0, our latest 10B+ foundation model for robots ⏱️ built on Harmonic Reasoning, new architecture that can think & act seamlessly 📈 strong scaling laws: more pretraining & model size = better 🌍 unprecedented corpus of 270,000+ hrs of dexterous data Read more 👇

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Generalist

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484,755 просмотров • 9 месяцев назад •via X (Twitter)

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Generalist AI unveiled GEN-0, an embodied foundation model family that hits a 7B-parameter “intelligence threshold”; small models ossify, larger ones keep improving, and is trained on 270,000+ hours of real-world manipulation data growing by ~10,000 hours/week. It introduces Harmonic Reasoning (think+act in continuous time), shows predictable scaling laws, runs across 6–16+ DoF robots, and an infra stack that ingests the equivalent of 6.85 years of experience per training day, promising faster adaptation with less post-training for factories, logistics, and service workflows.
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Generalist AI unveiled GEN-0, an embodied foundation model family that hits a 7B-parameter “intelligence threshold”; small models ossify, larger ones keep improving, and is trained on 270,000+ hours of real-world manipulation data growing by ~10,000 hours/week. It introduces Harmonic Reasoning (think+act in continuous time), shows predictable scaling laws, runs across 6–16+ DoF robots, and an infra stack that ingests the equivalent of 6.85 years of experience per training day, promising faster adaptation with less post-training for factories, logistics, and service workflows.

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Introducing a small portion of proprietary phenomics data from Recursion into the training of MolGPS, our foundation model for molecular property prediction, led to massive performance increases on new & existing benchmarks. Learn more about the impact of data & scale.
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Introducing a small portion of proprietary phenomics data from Recursion into the training of MolGPS, our foundation model for molecular property prediction, led to massive performance increases on new & existing benchmarks. Learn more about the impact of data & scale.

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Introducing INTELLECT-3: Scaling RL to a 100B+ MoE model on our end-to-end stack Achieving state-of-the-art performance for its size across math, code and reasoning Built using the same tools we put in your hands, from environments & evals, RL frameworks, sandboxes & more

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Excited to announce that we have raised $120M in our Series A to advance the frontier of general-purpose high-performance robots. 🤖 The new funding will accelerate progress towards our mission of bringing foundation-model powered robots to everyone, everywhere. Read more 👇
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Excited to announce that we have raised $120M in our Series A to advance the frontier of general-purpose high-performance robots. 🤖 The new funding will accelerate progress towards our mission of bringing foundation-model powered robots to everyone, everywhere. Read more 👇

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Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇
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Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇

Generalist

382,267 просмотров • 4 месяцев назад

✨ Meet our new open family of models: NVIDIA Nemotron 3 Open in weights, data, tools, and training, Nemotron 3 is built for multi-agent apps and features: • An efficient hybrid Mamba‑Transformer MoE architecture • 1M token context for long-term memory and improved reasoning • Multi‑environment reinforcement learning via NeMo Gym for advanced skill adaptation Plus NVFP4 pre-training, latent MoE, 1T tokens of data, and more. 📗Read the details in our tech blog: 🤗 Try the model on Hugging Face:
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🤖 LingBot-VLA 2.0 is now open-source — our next-gen embodied foundation model. 🔷 60,000 hours of high-quality pretraining data — combining curated robotic demonstrations and egocentric human operation videos 🔷 20 robot configurations across 17 brands — Astribot, Leju, Unitree, Franka, Fourier, Realman, and more 🔷 Whole-body DoF: heads, waists, dexterous hands, and mobile bases — enabling far more complex task scenarios 🔷 Inference under 130ms on RTX 4090 — developer events launching soon #EmbodiedAI #Robotics #OpenSource #VLA
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We’re introducing major updates to Gemini in @GoogleChrome for MacOS, Windows and Chromebook Plus. Built on Gemini 3, our most intelligent model, these powerful new AI features can help you multitask more easily and get the most out of the web 🧵
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We’re introducing major updates to Gemini in @GoogleChrome for MacOS, Windows and Chromebook Plus. Built on Gemini 3, our most intelligent model, these powerful new AI features can help you multitask more easily and get the most out of the web 🧵

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Very excited to share what our research team at Covariant has been working on: RFM-1, our latest Robotics Foundation Model. Built on top of high-quality multimodal data the Covariant robotics fleet has collected over the years, this project embodies our commitment to pushing the boundaries of how AI can interact with and understand the physical world. We're eager to share this milestone with the community and invite feedback and collaboration. Read more at
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Cerebras and Mayo Clinic are proud to announce a new state-of-the-art foundation model for genomics. The breakthrough was made possible by combining Mayo Clinic's extensive patient data with the Cerebras AI platform, training on over a trillion tokens to create complex genomic models that can guide personalized treatment decisions. Read more:
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Cerebras and Mayo Clinic are proud to announce a new state-of-the-art foundation model for genomics. The breakthrough was made possible by combining Mayo Clinic's extensive patient data with the Cerebras AI platform, training on over a trillion tokens to create complex genomic models that can guide personalized treatment decisions. Read more:

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Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!
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Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

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76,988 просмотров • 3 месяцев назад

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One of the why questions we’re always asking is how can we help operations employees access inventory more efficiently? We’re taking a big leap forward by introducing DeepFleet, an exciting new AI model that makes our one million+ robot fleet work smarter. Think of DeepFleet as an intelligent traffic management system for our fulfillment centers. Just like smart traffic systems reduce congestion in cities, DeepFleet coordinates our robots’ movements to find optimal paths and reduce bottlenecks. By leveraging our deep expertise in robotics and combining it with AWS tools like SageMaker, we’ve built an AI model that learns from our extensive data on inventory movement to make real-time decisions that improve robot travel time by 10% (which at our scale is pretty significant). What makes this particularly exciting is how AI and robotics create value across the board. For customers, it means faster delivery times and lower costs. For employees, it opens up new opportunities – our robots handle physically demanding tasks and heavy lifting, making our fulfillment centers safer, while our people take on more technical roles. This combination of robotics and generative AI is just getting started, and am looking forward to what we’ll invent next.

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🌟 Today we’re sharing the vision for a New Web3 — a more interconnected, human internet that drives new value systems and can lay the foundation for a new digital society. Here is the first of two articles, detailing the journey of our evolution and why we built new standards. This one is for the visionaries and dreamers of the initial Ethereum vision of a decentralised internet and shows how we can evolve it into a universal decentralized operating system, entailing the whole of web3. This one is slightly more technical. 👉 Read on Medium:
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Data for the Real World Diana & Austin Tindle AI has become superhuman at code, language, and images because it has enormous amounts of data to learn from. But our understanding of the physical world is still limited by sparse sensors and models built largely on intuition. Cheaper sensors and better foundation models could unlock entirely new datasets for energy, agriculture, logistics, construction, and the climate. Once you can accurately model the physical world, you can begin to control it.
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Data for the Real World Diana & Austin Tindle AI has become superhuman at code, language, and images because it has enormous amounts of data to learn from. But our understanding of the physical world is still limited by sparse sensors and models built largely on intuition. Cheaper sensors and better foundation models could unlock entirely new datasets for energy, agriculture, logistics, construction, and the climate. Once you can accurately model the physical world, you can begin to control it.

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22,185 просмотров • 29 дней назад

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Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks. Try the demo and learn more here:

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8 billion humans deserve an intelligence that doesn't blink when the signal dies. 🧠 Introducing QVAC Psy, our foundational models built on the mathematical stability of Psychohistory. With QVAC MedPsy, our local-first medical health AI model, we’ve proven that superior methodology beats raw parameter count. Our 1.7B & 4B models are delivering expert-level healthcare reasoning on consumer hardware. The "tiny brain" for the next galaxy is here. Fully open-source. Fully sovereign. Learn more

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