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Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies
NVIDIA details a three-computer robotaxi platform that Uber, Lyft, May Mobility, Mercedes-Benz and others are adopting to scale autonomous fleets.
NVIDIA says every major commercial robotaxi program runs on its stack, spanning training (DGX with Alpamayo VLA models), simulation and validation (Omniverse, Cosmos, AlpaSim on RTX PRO), and in-vehicle compute (DRIVE Hyperion 10 with dual DRIVE AGX Thor chips). Adding meta-action and chain-of-thought reasoning data to a VLA model reduced minimum average displacement error by 43%, from 2.08 to 1.18. Uber plans NVIDIA DRIVE Hyperion-based fleets across 28 cities by 2028, partnering with Autobrains, Avride, Lucid, May Mobility, Mercedes-Benz, Momenta, Nissan, Nuro, Pony.ai, Stellantis, Waabi, Wayve, WeRide and Zoox. DRIVE Hyperion 10 combines 14 cameras, nine radars, three lidars and 12 ultrasonics with redundant compute and NVIDIA Halos safety validation.
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Tick Group Weaponized Secure USB Drives to Target Air
Unit 42 says the Tick group weaponized South Korean certified secure USB drives with SymonLoader malware to reach air-gapped Windows XP systems.
Unit 42 discovered that the Tick cyberespionage group, which targets Japan and South Korea, compromised an ITSCC-certified secure USB drive made by a South Korean defense company and used a new loader named SymonLoader to extract a hidden executable from these drives. SymonLoader only infects Windows XP and Windows Server 2003, suggesting deliberate targeting of legacy air-gapped systems used by government and defense organizations. The group also delivered HomamDownloader and SymonLoader via Trojanized Korean and Japanese software sent as spearphishing attachments. Unit 42 believes the attacks occurred multiple years ago and that this malware is not part of an active campaign.
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Qwen-Drive 1.0 tells you why it brakes, just don't expect the explanation to match the maneuver
Alibaba's Qwen-Drive 1.0 adds 3D perception and planning modules to Qwen3.5-4B for driving tasks, though explanations often mismatch maneuvers.
Qwen-Drive 1.0, built on Qwen3.5-4B, combines spatial perception, traffic question answering, and route planning in one vision-language model, adding a bird's-eye-view perception module and a Planning Expert trained via staged fine-tuning and reinforcement learning. The paper finds text-image models do not inherently grasp 3D space; spatial accuracy only improved when the base vision-language model itself was trained on spatial tasks, while avoiding catastrophic forgetting of general knowledge. The cut reinforcement learning-trained version halved road-departure rate in simulation from 24% to 12%, and the model beats specialized driving models in most of Qwen's benchmarks, but its explanations sometimes conflate causes like distant red lights and crossing children, and results partly rest on self-designed tests. The work follows prior findings from PaLM-E and a UC Santa Cruz adversarial sign attack on DriveLM showing VLM driving models' reasoning and spatial gaps.
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Has anybody seen my keys? A key-hierarchy strategy for rack-level security
Oxide's RFD 0301 proposes a rack-level key hierarchy using Shamir secret sharing and a trust quorum to protect data-at-rest keys.
Oxide's request for discussion (RFD 0301) lays out a key-hierarchy strategy for rack-level security, deriving keys from a rack secret protected by Shamir secret sharing across a trust quorum of sleds, with keys exchanged over authenticated sprockets sessions. The document maps which keys protect control-plane data, metrics, Crucible extents, and authentication tokens, and defines open questions on key lifecycle, locality, and compromise handling. Future work includes sealing shares with the root of trust so an attacker would need to steal K whole sleds to reconstruct the rack secret.
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Viggle/Viggle-Animate — new model trending #28 on Hugging Face
Viggle released Viggle-Animate, a 33.1B MiniMax-H3 finetune replacing video characters from one repainted frame, rendering 124 frames in 26 seconds on one GPU.
Viggle-Animate replaces the character in a video using only a driving video and one of its own repainted frames, with no pose estimator, segmentation mask, face tracker, or text encoder. It is a 33.1B full finetune of MiniMax-H3's ref2va transformer, jointly distilled with DMD across two teachers split by noise level, so rendering takes three forward passes per clip. On a B200 GPU it renders 124 frames in 26 seconds, 6.1x faster per clip than Wan2.2-Animate-14B in matched comparisons. The method assumes no person-specific representation, so it generalizes beyond humans; a demo, research write-up, and ComfyUI nodes are available.
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