Research published in Science by Google DeepMind shows its AlphaProof Nexus independently solved 9 of 353 open Erdős problems, two of which had stood for 56 years, proved 44 conjectures in the OEIS integer sequence database, solved an open algebraic geometry problem, and improved known bounds for min-max optimization.
Google Research released SensorFM, a Large Sensor Foundation Model pretrained directly from unlabeled wearable data. The training data comes from 5 million users who consented to participate in the research, covering over one trillion minutes of multimodal sensor signals across more than 100 countries and over 20 Fitbit and Pixel Watch devices.
A team led by Stanford School of Medicine geneticist Gary Peltz published research in Advanced Science using Google DeepMind's Co-Scientist to screen existing drug literature for candidates that can be repurposed to treat liver fibrosis.
Google DeepMind released the Decoupled DiLoCo distributed training architecture, which splits large model training into asynchronously communicating compute islands, isolating hardware failures and sharply reducing bandwidth requirements.
Google Research proposes a framework for evaluating how well the behavioral dispositions of 25 LLMs align with humans, adapting standardized psychological questionnaires into situational judgment tests (SJTs), with human preference distributions provided by 550 participants.
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.