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Kunlun Tech's Skywork AI released Matrix-Game 4.0 under Apache-2.0: an engine runs the rules and physics while a causal video model renders low-poly frames into detailed game visuals.
AI-powered smart glasses promise to keep your phone in your pocket, but their usefulness depends on how much work they can take off your day. Meta ties its glasses to its own AI services, while Google is courting eyewear makers with Gemini.
LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
Figma is also releasing its code layers feature in open beta.
Figma introduced Figma Specs—a Markdown based editor for defining design systems—and opened its code layers feature to public beta at its first Config event in India.
The FT’s inaugural ranking of the UK’s fastest-growing companies shows the diverse businesses powering ahead against a volatile backdrop, in markets from health and wellness to fried chicken and digital ID. Plus: why management is key and finding growth in the ageing population
At least 90 minutes under general anesthesia on Tuesday briefly left Pope Leo unable to govern, spotlighting how the Catholic Church functions during a pope’s temporary incapacity.
The blend of property, infrastructure and fast-changing technology might be better suited to other kinds of funding.
Minute by minute AI monitoring is expanding in workplaces, turning older “bossware” into systems that can continuously log employee activity and productivity.
Peru is set to cancel its 2026 second anchoveta season after already suspending fishing in June and fully closing the first season in August, according to local reporting from Chimbote.
UK-based tech company is growing quickly but has encountered regulatory obstacles in Spain
While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which causal graphs remain identifiable (i.e., uniquely determined from the available data distribution) under various…
Video-based remote physiological measurement (RPM) has broad applications, while Vision-Language Models (VLMs) provide a promising opportunity to enable end-to-end physiological monitoring for future human-aware intelligent systems.
Benchmarks shape how model capabilities are measured and interpreted, yet their scores can obscure whose conventions, contexts, and judgments define successful performance.
As video models enable longer and more realistic interactive experiences, maintaining a world consistent with its history becomes essential. This requires both preserving previously observed content and ensuring coherent world evolution over time.
Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them. These choices can substantially affect measured performance and, consequently, decisions about subsequent pretraining.
Reinforcement learning (RL) has substantially improved the reasoning of autoregressive and discrete-diffusion language models. Language models that generate continuous token embeddings are emerging as a strong alternative, but RL post-training for them remains understudied.
Multimodal Large Language Models (MLLMs) have been widely applied in embodied agents and robotics for vision tasks. However, MLLMs suffer from limitations in spatial reasoning and perception.
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