Introducing VC-Attention: fast and accurate low-bit attention without retraining. On MiniMax-H3, VC-Attention speeds up attention by 1.6× on B200 and 1.5× on B300 over FlashAttention-4, with better fidelity than SageAttention2. It also works with existing sparse attention methods. Two key innovations: • V-Smooth reduces value quantization error. • ExpCast-FP8 speeds up softmax. Nunchux Attention, our proprietary extension, pushes the speedup to 1.9× on B200 and 1.8× on B300. Blog: http://www.nunchux.ai/blog/attention-is-the-video-bottleneck Technical Report: http://arxiv.org/pdf/2609.15810 Joint work by researchers at MIT, CMU, UC Berkeley, Stanford, and NVIDIA.

I've had the most wonderful time working on this project for the last few months. This was (equally) co-led w/ @JMateosGarcia , @alexolegimas and a fantastic team.

🍾🍲 Saturday Robotics x IROS 2026 — Robotics Research Night 👉🏻 https://luma.com/tzbw7n61 We’re bringing a high-signal evening of robotics research to Pittsburgh on September 28. After a full day at IROS, we’ll bring together researchers, engineers, founders, students, and investors for technical discussions, networking, and a series of ~10-minute lightning talks. Tentative preview of the current lineup: 🤖 1. PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball Gary Yang @lzyang2000 (@Caltech) Perception-aware reinforcement learning + Control Barrier Functions for whole-body humanoid safety. Demonstrated on a Unitree G1, with 19/20 successful dodges and zero falls in real-world experiments. 🧠 2. How In-Context Learning Is Reshaping Robot Learning Data at Scale AaronLi (@RhodaAI) Exploring how in-context learning can change the way we think about robot learning data, scaling, and generalization. 🧪 3. X2Real: An eXtensive Simulation Benchmark for Real-World Generalist Policies Liangwang Ruan (@XSquareRobot) A new simulation benchmark built around faithfulness, diversity, and fairness, with 44 hierarchical long-horizon tasks across 10 capability dimensions and a reported 0.84 simulation-to-real correlation. 🦾 4. Rethinking Generalist Robotic Manipulation: Architecture, Data and Inference for Real-World Deployment Peiyan Li (Chinese Academy of Sciences, @CAS__Science) 3D VLA architectures, memory augmentation, ego/UMI human priors, large-scale robot pretraining, and inference-time contextual learning for deployable generalist manipulation. 🎯 5. HiRE: Hindsight Reward Editing for Policy Finetuning Haoyi Niu @t641769919 (@UCBerkeley) Accepted at CoRL 2026. A training-free approach to reward editing that uses successful and failed trajectories to identify “trap states” and provide denser, control-aware feedback for RL. 🔥 6. Lightning Talk — Open Slot We’re opening one additional slot for a technically deep research talk, new project, frontier paper, demo, open problem, or startup technical insight. 10 minutes. A few slides. One sharp technical idea. No fluff. Topics include World Models, Physical AI, Humanoids, VLAs, Robot Foundation Models, Manipulation, RL, Simulation & Sim-to-Real, Spatial Intelligence, Computer Vision, and Embodied AI. 📍 Pittsburgh 📅 September 28, 2026 🕠 5:30–9:30 PM 🍾 Networking + Technical Talks + Research Discussion 📩 junfanzhu98@gmail.com See you in Pittsburgh. 🤖 #IROS2026 #Robotics #PhysicalAI #RobotLearning #WorldModels #HumanoidRobotics #VLA #EmbodiedAI #RobotFoundationModels

What happens when AIs become smarter than us? Why would they keep humans around if given the choice? Our new paper argues that only trying to control AIs is a limited strategy, and that a stable, mutualistic human-AI future may be possible.