Qualcomm lands Meta as first named customer for its Dragonfly data centre chips
Qualcomm signed Meta as the first named customer for its Dragonfly data-centre chips, a foothold in the AI accelerator market long dominated by Nvidia.
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FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation
Sparse voxel representation has emerged as a scalable foundation for image-to-3D Gaussian Splatting (3DGS) generation, yet current methods struggle to...
InSight: Self-Guided Skill Acquisition via Steerable VLAs
Vision-language-action (VLA) models can learn manipulation skills from demonstrations, but their capabilities are bounded by the skills in the training...
OpenThoughts-Agent: Data Recipes for Agentic Models
Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable...
AIR: Adaptive Interleaved Reasoning with Code in MLLMs
Following the paradigm shift initiated by OpenAI o3, interleaved reasoning with code to enhance multimodal large language models (MLLMs) has become a...
CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation
Humanoid loco-manipulation is often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming locomotion...
DeepSeek Just Solved AI's Billion Dollar Problem
DeepSeek claims a breakthrough on one of AI's most expensive bottlenecks, a problem often pegged at a billion dollars of compute to attack.
OpenAI launches new initiative to help find and patch open-source bugs
OpenAI launched an initiative to help find and patch bugs in open-source software, extending model-assisted code review to the wider ecosystem.
Semantic Browsing: Controllable Diversity for Image Generation
Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated...
The AI world is getting ‘loopy’
The AI world is getting 'loopy' as models increasingly train on and cite each other's outputs, tightening a self-referential loop.