Meituan LongCat-2.5 brings a powerful 1.6 trillion-parameter multimodal AI model with advanced coding, visual understanding and autonomous agent capabilities.
Meituan released LongCat-2.5 on Monday — a 1.6-trillion-parameter multimodal AI model built for long-horizon autonomous coding tasks — and trained the entire system on domestically produced Chinese AI accelerators for the second time in a row.
Borrowing from disciplines like economics could help AI safety researchers control AI models, even if they don't understand them. A new approach known as "reverse game theory" aims to create incentives for powerful AI models that make following the ...
Kaspersky researchers have identified attacks using CyberStrikeAI, an AI-powered framework that helps cybercriminals coordinate intrusions and accelerate access to compromised networks.
SemiAnalysis’s GDPx benchmark tested 13 frontier AI models on 206 real professional tasks. Best score: 12%. Finance and legal returned zero across every model.
General-purpose mobile manipulation requires a combination of capable robot hardware, scalable demonstration collection, expressive whole-body control, and effective policy learning. Yet these components are often developed independently, creating mismatches that limit the capabilities of the complete system. We present Pixel, a co-designed full-stack system for learning general-purpose mobile manipulation. Pixel combines a low-cost wheeled humanoid with human-aligned kinematics and compliant grippers, multiple teleoperation and robot-free human data-collection interfaces, a unified whole-body controller, and a common policy learning pipeline across human and robot demonstrations. A hierarchical learning stack further enables language-conditioned visuomotor control and long-horizon task execution. We evaluate the system across hardware, data collection, and policy learning, and demonstrate mobile-manipulation behaviors requiring dexterity, active perception, navigation, and whole-body coordination. We hope that Pixel provides an integrated and extensible foundation for studying how robot hardware, data, control, and learning can be designed together for robot learning. More…
Idol turns one picture of an object into a 3D Gaussian splat. Give it a render, a generated image or a photo: it removes the background, frames the object and generates up to 1,048,576 Gaussians, written as a standard 3DGS .ply. It runs on consumer GPUs, using 7.2 GB of VRAM…
US artificial intelligence giant Anthropic’s initial public offering prospectus has exposed a vast profitability gap between it and rivals Z.ai and MiniMax, highlighting the challenges facing China’s AI model developers in turning technological advances into sustainable…
Model-free reinforcement learning can acquire contact-rich robotic manipulation skills through trial-and-error interaction, but it often requires the policy to learn both task strategy and low-level motion generation. In this setting, the action representation is critical…
Recurrent Transformers increase computational depth through temporal recurrence, feeding each token's high-level hidden state into the computation of the next. This raises a natural question: is additional computation better spent on more temporal steps or greater physical…
Access to academic support is a key determinant of student success, yet students experience it unequally: some readily seek help from lecturers or tutors, while others hesitate due to anxiety, fear of judgement, uncertainty about expectations, or low confidence in their…
Fraudulent job posting detection aims to identify job advertisements that are corrupted either through fake content, misleading information, or negative intent, disrupting the online eco-system of job-seekers and employers. Existing studies in this domain lack effective methods…