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TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking

Researchers propose TFTrack, a template-free LiDAR 3D single object tracking framework cutting FLOPs ~50% while running at ~120 FPS.

TFTrack is the first template-free framework for 3D Single Object Tracking, dropping template-search pairings and complex motion modeling in favor of the prior bounding box center plus geometric alignment. It ships in three variants (TFTrack-Voxel, TFTrack-Pillar, TFTrack-Point) covering sparse and dense 3D representations. On KITTI and nuScenes it is competitive with leading template-based trackers while reducing FLOPs by about 50% and running near 120 FPS. Code is released, targeting real-time deployment in embedded robotics such as autonomous vehicles.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research

Microsoft Tracks MacSync Stealer by Its Behavior, Not Its Domains

Microsoft correlates over 30 rotating domains to track MacSync Stealer, which steals passwords, SSH keys, wallets and AWS credentials.

Microsoft Defender Experts tracked MacSync Stealer, a macOS information stealer, by analyzing recurring behaviors rather than individual domains, linking over 30 domains to the campaign. The infection chain uses the ClickFix social engineering technique, tricking victims into pasting commands in Terminal, then uses curl, AppleScript, and native macOS tools to deploy the payload. The stealer targets Keychain data, browser passwords and cookies, SSH keys, AWS credentials, Kubernetes configurations, and Ledger and Trezor wallet data, staging data before exfiltration via chunked HTTP PUT requests. RST Cloud identified rapid C2 infrastructure replacement after initial public disclosure.

Security Affairs · 28d agoMalware

ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

ASTRIL-MPC combines learned kinematics, NMPC, and LLM-guided safety-checked retuning for articulated tracked robot traversal in search-and-rescue.

ASTRIL-MPC is a language-guided neural-kinematic model predictive control framework for autonomous traversal of articulated tracked robots in urban search and rescue. A learned kinematics model predicts short-horizon task-state increments, NMPC plans with feasibility constraints, and an LLM proposes bounded, safety-checked updates to weights and bounds. The compiled predictor enables a full control cycle within 100 ms, improving traversal-quality scores by up to 71% over non-adaptive NMPC and 67% over a PPO baseline while eliminating measurable collision impacts.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research

US military disabled ad tracking on troops’ devices following reports of targeted attacks

US DoD disabled ad tracking on troops' devices after foreign adversaries targeted service members using commercially bought location data.

Per letters shared with Senator Ron Wyden, the Army, Air Force, Navy, Marine Corps, and Special Operations Command disabled advertising IDs on iPhones, Android devices, and Windows computers across the federal military enterprise. The protections rolled out earlier in 2026, with the Air Force implementing changes in July, to prevent adversaries from exploiting app-derived location data sold through data brokers. Wyden warned that personal devices brought onto military bases could still expose troops and facilities, and noted the intelligence community and FBI buy such data without warrants.

TechCrunch · Security · 12d agoPolicy & legal

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.

Hugging Face trending models · 15d agoModel release