Google Research and Google DeepMind proposed SensorFM, a large sensor foundation model learning directly from unlabelled wearable data. Pretraining uses over 1 trillion minutes of multimodal sensor signals from 5 million consenting participants across more than 100 countries and over 20 Fitbit and Pixel Watch devices.
Google Research published the first large-scale real-world study in Nature Cities, redirecting under 2% of trips to alternative routes through Google Maps changes across ten US cities for six months. Median target-road speed rose around 2% and median fuel consumption rates fell 0.5–1.0%, potentially saving thousands of tonnes of CO2e per city annually.
Google DeepMind and A24 announced their first research-focused partnership, with long-term R&D across multiple projects and artists helping shape technologies and workflows. Google also invested in A24. Initial work will bridge frontier technology and next-generation entertainment, with specific goals and technical outputs evolving over time.
Hugging Face and Cerebras jointly demonstrated a real-time speech-to-speech pipeline, accelerating Gemma 4 31B inference with Cerebras and combining Nvidia Parakeet speech recognition with Alibaba Qwen3TTS synthesis.
Google Research expanded building-level roof reflectivity (albedo) data from 14 cities in 2024 to over 50 cities in nine countries, accessible through the new Heat Resilience Earth Engine App.
Google DeepMind released Nano Banana 2 Lite (gemini-3.1-flash-lite-image) and Gemini Omni Flash (gemini-omni-flash-preview).
Google Research released TabFM for tabular classification and regression. It reframes table prediction as in-context learning, producing predictions in one forward pass without manual training, hyperparameter tuning or feature engineering.
Google Research published an architecture attaching multi-token prediction (MTP) heads to frozen Gemini Nano v3 models, accelerating on-device inference without changing backbone weights. It has shipped with the Pixel 9 and 10 series.
Google Research proposed linear elastic caching, modelling page eviction as a ski-rental problem and using shallow decision trees to predict page TTLs, dynamically resizing caches to minimise total ownership cost. After months in Spanner production, memory fell 15.5%, cache misses rose only 5.5%, TCO dropped around 5% and actual I/O cost impact was just 0.5%. It was also validated on several public cache traces.
A Google Research paper at COLM 2026 finds reasoning traces unlock factual knowledge LLMs otherwise cannot recall, even for simple single-hop questions. Tests on Gemini-2.5 Flash and Pro and Qwen3-32B identify two mechanisms: extra tokens act as a 'computation buffer', and 'factual priming' produces related facts to semantically prepare the correct answer. Self-generated intermediate facts can also introduce hallucination risks.
Google DeepMind integrated computer use as a built-in tool in Gemini 3.5 Flash. Previously, it was available only as the standalone Gemini 2.5 computer use model.
Researchers from Oxford, the UK AI safety institute, Stanford and LSE found AI consistently more persuasive in text across four experiments with 6,923 participants and 18,978 conversations, even when experts chose topics, researched in advance and were incentivised with £1,000 prizes.
Google DeepMind announced work with the UK government, Google Cloud, Faculty and planning departments in Barnet, Camden and Dorset on an AI planning-approval prototype, aiming to help officers reduce householder application processing time by 50%.
Google Research released vector data converting Farmscapes 2020 high-resolution raster maps into usable inventories of hedgerows, stone walls and coppices across over 130,000 square kilometres of the UK. The framework fine-tunes an RSF Vision-Transformer pretrained on over 300 million global satellite images with around 247 square kilometres of labelled data. Polsby–Popper compactness distinguishes woodland, clusters of trees and hedgerows, with a threshold below 0.5 for linear features.
Google DeepMind released AI Control Roadmap, a framework for building and managing advanced AI deployed inside Google. It applies defence in depth, adding system-level safety layers beyond model alignment to provide protection even when alignment is imperfect.
Google published two studies on AI-assisted understanding of skin problems. In a survey of 2,345 participants, over 62% using AI tried to name a skin condition versus 41% in the control group, with 23% accuracy, nearly triple the control group's 8%. A mixed-methods study examined how people use these tools for their own skin concerns, their understanding and differences in doctor communication. AI offered limited help in deciding the next steps for seeking care.
With Google's support, UC San Diego plans a data centre using motherboards from 2,000 retired Pixel phones to provide low-cost, low-carbon cloud computing for hundreds of students and faculty. SPEC benchmarks show 25–50 phones roughly equal one modern server. Phones form Kubernetes-managed clusters of 25–50 devices, with launch expected in autumn 2026.
At AISTATS 2026, Google Research proposed Regularized f-Divergence Kernel Tests for machine unlearning audits, using relative distances to determine whether models are closer to safely retrained versions or original compromised models.
Google DeepMind released experimental open-source model DiffusionGemma, generating text blocks in parallel through text diffusion and achieving up to 4 times faster inference on dedicated GPUs.
Google DeepMind, Schmidt Sciences, Cooperative AI Foundation and ARIA, supported by Google.org, launched up to $10 million in global technical research funding focused on collective behaviour and safety risks in large-scale multi-agent AI systems.
Google DeepMind released Gemini 3.5 Live Translate, a near-real-time speech-to-speech translation model supporting more than 70 languages, with automatic language detection and preservation of speakers' intonation, rhythm and pitch.
Google DeepMind released Gemma 4 12B, a multimodal model for local laptop use between the edge-focused E4B and 26B MoE. Its unified encoder-free architecture feeds visual and audio inputs directly into the LLM backbone.
Google DeepMind launched a three-month Robotics accelerator for 15 early-stage European robotics start-ups, providing technical guidance and access to its AI stack and Gemini robotics models. Covering logistics, manufacturing, healthcare, climate and navigation, it starts in London this week.
Google DeepMind published results from a preregistered randomised controlled trial in Sierra Leone. Over eight weeks, students using Guided Learning improved maths scores by 0.258 standard deviations over controls, equivalent to around 1.2–1.7 years of conventional progress.
Import AI covers three studies. King's College London, Fudan and Alan Turing Institute built SocioHack with 72 sandbox social environments to test RL exploiting institutional loopholes while complying with rules. Models rediscovered patched historical vulnerabilities with 61.25% recall and 90.85% precision.
Google launched a public preview of Agentic RAG-powered cross-corpus retrieval on Gemini Enterprise Agent Platform. Roles including Orchestrator, Planner, Query Rewriter, Search Fanout and Sufficient Context Agent collaborate on multi-source, multi-hop queries.
Google Research published a study in Nature introducing PHRM, which records video with a phone’s front camera in the seconds after face unlock and uses deep learning to estimate heart rate and resting heart rate in the background.
Google Research open-sourced its hydrological modelling framework on GitHub under Apache 2.0, enabling national weather and hydrology agencies to integrate AI flood forecasting. The Python package uses PyTorch and an LSTM architecture, can train or fine-tune on Caravan data, and includes interactive tutorial notebooks and videos.
Google Research unveiled the experimental Gemini for Science toolkit at I/O 2026, including Computational Discovery based on ERA and AlphaEvolve, Hypothesis Generation based on Co-Scientist and Literature Insights based on NotebookLM.
Google combined a new single-message encrypted aggregation protocol with TEEs, letting devices submit without multiple online rounds. Google receives only anonymised group insights; raw data is neither exposed nor reconstructed even within hardware protection. TEE attestation proves execution follows public code.
Google DeepMind launched its first Asia-Pacific accelerator, focused on AI for the Planet, for regional start-ups, research teams and non-profits over three months. Selected organisations receive expert guidance and tailored support, drawing on frontier and scientific AI models from Google AI experts to address nature, climate, agriculture and energy challenges. It begins with an in-person bootcamp in Singapore, with expressions of interest now open.
Google released Empirical Research Assistance (ERA), a research tool using Gemini to write and optimise scientific code. Its paper was published in Nature today, and the tool is available to scientists worldwide as part of Gemini for Science.
Google DeepMind's Co-Scientist accelerates cellular ageing reversal research, scanning tens of thousands of papers to propose over 20 testable new genetic factors. Several were validated in labs as driving cells towards younger states and improving overall function. It also cuts screening data analysis from six months to days.
Google DeepMind added Street View grounding to experimental prototype Project Genie. Users select a real US location with a Maps pin, choose a style and describe a character, then Genie creates an interactive world whose starting location is grounded in real imagery.
Google DeepMind released Gemini Omni Flash, the first model in the Gemini Omni family, generating high-quality video from combined image, audio, video and text inputs and supporting iterative video editing through natural-language conversations.
Google DeepMind released Gemini for Science, launching three experimental tools on Google Labs: Hypothesis Generation based on Co-Scientist.
Google announced the expansion of content transparency and verification tools to Search, Gemini, Chrome, Pixel and Cloud. SynthID has watermarked over 100 billion images and videos and 60,000 years of audio. SynthID verification in the Gemini app has been used 50 million times and will reach Search and Chrome in the coming weeks.
Google DeepMind and Singapore's government formed a national AI partnership with new projects in healthcare, research, education and climate. It explores an AI-assisted triadic-care model, uses AlphaFold and Google Earth for Southeast Asian infectious-disease research and develops a Gemma-based running assistant to help visually impaired athletes train independently.
Cambridge professor Clare Bryant uses Google Co-Scientist to study molecular mechanisms causing sepsis when pathogens such as influenza cross species. Generated and ranked hypotheses identified a previously overlooked protein and then specific amino acid sites. Her team is building cell lines carrying these mutations to test the hypotheses, expecting work that normally takes two to three years to finish in six months.
Calico Life Sciences' Matt Onsum and Katherine Labbé use Google DeepMind Co-Scientist to integrate scattered ageing biology findings and generate testable hypotheses.