Google released research prototype AgentHands, published at CHI 2026, mapping LLM reasoning to speech-synchronised hand animations in XR headsets so agents can point and demonstrate object operations in 3D. It combines environment perception, a gesture event library, gesture-embedding reasoning and local synchronised execution, supporting deictic, iconic and expressive gestures.
Google introduced Biomarker Discovery Framework, a human-supervised multi-agent system organising candidate biomarker prioritisation into iterative research cycles. Across 9,279 participant observations in three cohorts, it automatically identified 41 candidate digital mental health biomarkers and 25 metabolic candidates, including an association between sleep-duration variability and PHQ-8 severity (ρ = 0.252).
Google DeepMind partners with game studios to prototype breakthrough AI gameplay.
Google Research proposed Mobility-Embedded POIs (ME-POIs), combining anonymised mobility patterns with text descriptions to generate place embeddings encoding both identity and dynamic function.
Google Research proposed PhotoScan, a deep learning framework that estimates body fat percentage, A/G ratio and V/S ratio from ordinary 2D phone photos to predict insulin resistance.
Google DeepMind released Gemini 3.7 Flash, positioning it as its strongest workhorse model for coding and agents, just three weeks after Gemini 3.6 Flash.
Introducing sign-language-to-text (SL2T), our breakthrough model powering new sign language features for Deaf and hard of hearing users.
Google Research's knowledge profiling framework evaluated 13 LLMs on WikiProfile's 2,150 Wikipedia facts. Gemini-3-Pro and GPT-5 encoded 95–98% but failed direct recall of 26–34%, and still failed 11–12% with thinking enabled. It argues frontier factual errors arise more from knowledge accessibility than absence, shifting the bottleneck from acquisition to use.
Google Research released AMIE (Video), built on Gemini and Project Astra, for real-time video clinical consultations. It can perceive non-verbal cues and guide virtual physical examinations.
Google DeepMind released the WeatherNext AI model, achieving state-of-the-art cyclone track, intensity and wind-field structure forecasts and adding an average extra day of warning, equivalent to roughly a decade of meteorological progress.
Google Research released the Chain-of-Evidence (CoE) verifiability framework, implemented in a Science One Framework prototype, with automated CoE Audit metrics.
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.
Google DeepMind released its next-generation music model Lyria 3.5 on Google Flow Music. It claims improvements in musicality, lyrics, vocals and creative control, including more complex and natural melodic structures, lyrics with better prompt adherence and structural awareness, more expressive vocals with clearer pronunciation, and easier control of rhythm and duration.
Google DeepMind released Gemini Robotics 2, a next-generation robot intelligence layer achieving whole-body control of a complete humanoid robot for the first time, with fine manipulation using both hands and grippers.
Google Research published SymptomAI research, using five Gemini Flash 2.0 agents with different questioning strategies for symptom interviews and differential diagnosis, involving 13,917 participants.
Google Research published a study in Nature proposing a reinforcement learning framework in which agents continuously learn from quantum error-correction detection events, dynamically adjusting thousands of control parameters during computation to counter drift.
Google commits $40M in AI tokens and credits for the Genesis Mission
We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.
The singularity will be seen in hindsight as an interregnum
Google introduces Gemini 3.5 Flash Cyber, a lightweight cybersecurity model to find and patch vulnerabilities.
Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience and AI models.
A Google Research ICLR 2026 paper traces diffusion model creativity to score smoothing from regularisation such as weight decay. Smoother learned score functions let denoising interpolate between training points, generating new samples rather than simply memorising them.
Google and AIM launched ATL Saathi, a Gemini-powered AI tool empowering Indian educators in robotics labs.
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%.
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.
How religious are beliefs in the singularity?
UK government partners with Google DeepMind to build a new AI-powered prototype aimed at faster housing decisions.
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.
Securing internal systems with an AI Control Roadmap, combining traditional safeguards and real-time monitoring.
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%.
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.