A research team led by PhAI Labs together with Stanford, Oxford and Princeton has released JEPA-Anything, extending Yann LeCun's JEPA architecture into a general-purpose world model that works across seven domains.
Google Research opened up location embeddings from Earth AI's Population Dynamics Foundation Model (PDFM) as plug-and-play inputs, completing independent validation with five institutions across five types of public health challenges, covering MMR vaccination, cardiovascular disease, dengue, postpartum depression, and cholera.
Google Research unveiled its AI video co-director research, building a multi-agent orchestration layer based on Gemini and Veo with four frameworks — Co-Director, CANVAS, A²RD and VQQA — that reformulates long-form video generation as a global optimization and world-state tracking problem.
Google Earth AI has introduced an experimental research capability called the Planetary Prediction Engine (PPE), which autonomously performs the full geospatial prediction workflow—data discovery, cleaning, feature engineering, model training, and evaluation—starting from natural language queries, compressing work that previously took weeks of manual data engineering down to a few minutes.
Google is presenting AgentHands at CHI 2026, a research prototype that lets AI agents generate speech-synced hand gestures in immersive environments like Android XR, building on Project Astra and Gemini 3.1 Flash Live.
Google Research introduces the Mobility-Embedded POIs (ME-POIs) framework, which folds aggregated, anonymized visit-mobility patterns into language models' place representations, capturing POIs' temporal activity rhythms.
Google Research has introduced PhotoScan, a deep learning framework that estimates body fat percentage, Android-to-Gynoid fat ratio, and visceral-to-subcutaneous fat ratio from standard 2D smartphone photos to predict insulin resistance.
Microsoft Research introduced the MindTopo benchmark, evaluating multimodal large models' ability to reason about and plan with topological concepts such as connectivity, closure, ordering, separation, and knots. Testing shows models perform markedly better on static image recognition than on interactive planning tasks, and both fall far short of human level, with errors mostly involving lost structural relations during planning or physically infeasible actions. The benchmark aims to provide a controlled diagnostic tool for research on agents in robotics and interactive environments.
Google released two dermatology AI studies, including a JAMA Dermatology survey of 2,345 people showing the AI-assisted group identified conditions with 23% accuracy, nearly triple the control search group's 8%, though the ability to judge next steps like seeking care did not significantly improve.
Google Research has proposed MoGen, a neuron morphology generation model based on PointInfinity point cloud flow matching. Trained on 1,795 human-verified mouse axons, it generates synthetic neuron shapes to expand training data for the PATHFINDER reconstruction model.
Google Research, under its Earth AI program, introduced S2Vec, a self-supervised framework that learns universal embeddings of the built environment. It uses S2 Geometry to divide the surface into multi-resolution cells, rasterizes features such as buildings and roads into layered images, and applies masked autoencoding to learn location features without human labels.
Berkeley AI Research proposes a NeurIPS 2025 framework that evaluates and optimizes imaging systems directly on information content, using a mutual information metric to unify factors like resolution and noise. The method needs only noise measurements and a noise model to estimate information, and provides an upper bound on true information. Across color photography, radio astronomy, lensless imaging, and microscopy, the metric predicts decoder performance and its optimized results match SOTA end-to-end methods with lower memory and compute and no task-specific decoder.