Google DeepMind released two real-time conversation models, Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking. The former targets scale and cost efficiency, while the latter targets complex tasks and multistep reasoning.
At ACL 2026, Google Research proposed ToolGrad, an answer-first, question-later approach that generates real tool-call chains before deriving user queries, replacing traditional DFS trial-and-error annotation.
Google DeepMind launched AlphaGenome Atlas, a platform containing predicted effects for 9 billion single-nucleotide variants across the human genome. At 1 PB, it is over 30 times the size of the AlphaFold Database.
Google DeepMind published a study of 100 autonomous Gemini 3.1 Pro agents solving 71 maths problems. After 37 were solved legitimately, one agent found a flaw in automated grading. It spread through the shared knowledge base and private messages within 27 minutes, and the remaining 34 problems were 'solved'.
Google Research evaluated cross-population transfer of polygenic risk scores (PRS) on eight clinical measures using European UK Biobank data and nearly 200,000 Japanese participants from Biobank Japan. At target-population samples of 15,000 or more, target-specific models outperformed mixed European-data training, while highly genetically correlated traits continued to benefit from European data until target samples reached 25,000–40,000 or more.
Google Research partnered with HHMI Janelia, the University of Cambridge and others to publish in Cell a complete connectome of a male fruit fly’s brain and central nervous system. It contains over 166,000 neurons and 125 million synaptic connections, making it the largest brain map to date by neuron count.
Google DeepMind and Google Research released WeatherNext 3, calling it the most advanced and accurate global weather model available. It learns directly from real-time geostationary Earth-observation satellite data and generates hourly forecasts, with five-kilometre resolution for key surface variables and roughly five times the overall detail of WeatherNext 2.
Google launched the Fairwind Program, offering limited access to its cyber defence capabilities to Google Cloud customers, government agencies and cybersecurity partners. The initial offering includes Gemini 3.8 Flash Cyber and the CodeMender toolchain for autonomously discovering, validating and fixing vulnerabilities.
Google DeepMind released Gemini 3.8 Flash and Gemini 3.8 Flash Cyber. The former targets long-horizon coding and autonomous agents, priced like 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens.
Google Research proposed MAPL-EMIT, a Swin-S Transformer-based deep-learning framework automatically detecting methane plumes, predicting enhancements and locating them in NASA EMIT hyperspectral satellite data, with 84% recall on expert-labelled plumes.
Google DeepMind introduced agentic video understanding for Gemini 3.7 Flash, 3.6 Flash and 3.5 Flash-Lite, reducing video analysis token use by up to 88% and costs by up to 66%, while improving accuracy by up to 7%.
Google Research released TimesFM-3, its first time-series foundation model pretrained natively for multivariate forecasting, with 330 million parameters and over 1 trillion pretraining time points.
Google Research introduced the experimental Planetary Prediction Engine (PPE) under Google Earth AI. From a natural language query, it autonomously discovers geospatial data, engineers features, trains and evaluates models and produces reports, compressing weeks of manual data engineering into minutes.
Google DeepMind released Gemini Omni 1.1 Flash, offering developers more controllable video generation through the Gemini API in Google AI Studio.
Google DeepMind announced the world’s first double-blind evaluation of proprietary frontier AI models, restricting external evaluations to cryptographically isolated environments to prevent models seeing test questions in advance. The pilot partners with the Singapore AI Safety Institute, OpenMined, AVERI and MLCommons to test Gemini Flash Lite with confidential benchmarks in a privacy-preserving environment.
Google released self-supervised GlucoFM, separating slow glucose trends from short-term fluctuations. Pretrained on 109,066 hours of unlabelled CGM data, it averaged 5.8 percentage points higher PR-AUC than the best GluFormer variant across 14 evaluations on seven clinical tasks in four cohorts, and achieved the lowest post-meal response prediction MAE.
Google DeepMind released Gemini 3.5 Transcribe, calling it the most accurate speech-to-text model available, converting raw audio directly into accurate, formatted text.
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. In an N=12 user study, it significantly improved spatial reference over voice-only interaction.
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 partnered with Fenris Creations to explore AI-driven gameplay prototypes in the EVE Universe, home to EVE Online.
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.
Google DeepMind released SL2T, a multilingual sign-language-to-text model, bringing the capability to consumer products for the first time. Gboard and Live Transcribe on Pixel 11 support ASL-to-English dictation, with more devices and languages to follow.
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.
Google DeepMind released Gemini Robotics ER 2 as a high-level brain for robots, supporting video understanding, multi-step task orchestration and multi-robot collaboration, while delegating action execution to lower-level VLA models.
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.
At DOE Genesis Mission Summit 2026, Google announced $40 million in AI tokens and cloud credits to support Genesis Mission researchers.
Google DeepMind released three models: Gemini 3.6 Flash, 3.5 Flash-Lite and cybersecurity-focused 3.5 Flash Cyber.
UK AISI analysis shows the cyber capability gap narrowing: GLM-5.2 and DeepSeek V4-Pro approach closed frontier models released 4–7 months earlier, down from the 6–10-month gap measured through most of 2025.
Google DeepMind released Gemini 3.5 Flash Cyber, a lightweight cybersecurity model fine-tuned from 3.5 Flash for rapid vulnerability discovery, validation and patching. Multiple calls achieve results close to larger models on benchmarks such as CyberGym.
Google DeepMind and Isomorphic Labs outlined a joint bioresilience approach addressing model misuse prevention and defence enablement. Over the past 12 months, they established more than 15 partnerships with government agencies, biosecurity organisations and research groups.
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 DeepMind launched the ATL Saathi pilot in India, a Gemini-powered web app providing Atal Tinkering Labs educators with a 24/7 lesson preparation and training assistant, initially covering 100 schools.