Google Research introduced PhotoScan, an investigational deep-learning system that estimates body-fat percentage, Android-to-Gynoid fat ratio and Visceral-to-Subcutaneous fat ratio from standard 2D smartphone photos. The system was pre-trained on more than 35,000 UK Biobank participant records and fine-tuned using a cohort of 677 adults. It is intended to estimate body-composition markers associated with insulin resistance, which is commonly defined by a HOMA-IR score above 2.9 and can precede type 2 diabetes by years. In five-fold cross-validation on the PhotoBIA cohort, PhotoScan produced a mean absolute error of 2.15 for body-fat percentage, compared with 2.91 for smartwatch bioelectrical-impedance analysis. Independent validation in the MetabolicMosaic cohort produced errors of 2.13 for body-fat percentage, 0.085 for the Android-to-Gynoid ratio and 0.085 for the Visceral-to-Subcutaneous ratio. A gradient-boosting model using demographics plus PhotoScan metrics reached an AUROC of 0.760 for identifying insulin resistance, versus 0.692 for demographics alone and 0.773 for demographics plus DXA scan data. Adding smartwatch bioelectrical-impedance data did not improve insulin-resistance classification. Google describes PhotoScan as a research prototype rather than a clinical screening product.

OpenAI's GPT-5.6 lineup includes the Sol, Terra, and Luna models, with a focus on enhanced computer use and the ability to navigate desktop applications. The models demonstrate improved visual understanding, which will be evaluated using an upcoming visual language model benchmark.

FROGS_ is a benchmark for SVG (Scalable Vector Graphics) focusing on the Habsburg jaw, featuring structural labels and editorial interpretations of anatomical features such as a "massive protruding mandible" and a "recessed" upper lip. The SVG includes a linear gradient definition for skin tones.

Explorative Modeling introduces a third pretraining axis for generative models, enhancing end-to-end generation capabilities. Increased exploration leads to improved performance across images, video, and language, with benefits scaling alongside model size.

FLUX 3 is a multimodal foundation model that learns from images, videos, and audio within a unified architecture. It aims to create a comprehensive representation of the world by understanding object interactions, movements, and corresponding sounds.

AI systems can surpass humans in various tasks but utilize different cognitive processes, allowing for the detection of AI agents and bots. Despite advancements in AI, CAPTCHAs remain effective in certain scenarios, as visual language models can recognize specific objects but may struggle with more complex tasks that require human-like reasoning.

GLM-5V-Turbo is a foundation model designed for multimodal agents, enhancing their capabilities in language reasoning and perception across diverse contexts. The model aims to improve the performance of agents in real-world applications by integrating various modalities.

An AI model developed at the Mayo Clinic detected abnormalities on CT scans up to three years before patients were diagnosed with pancreatic cancer. This capability may allow for earlier intervention, improving treatment outcomes.

Machine learning techniques have identified previously unrecognized transient astronomical phenomena in historical observatory images. These phenomena consist of transient, star-like point sources that appeared and disappeared over short timescales before the launch of Sputnik.

LATENT enables the simulation of highly-dynamic tennis rallies by human athletes, showcasing versatile skills in a competitive environment. Developed by researchers from Tsinghua University, Peking University, and other institutions, it utilizes advanced AI techniques for real-time performance.