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Google Research researchers have developed MAPL-EMIT, a deep learning framework that automatically detects and quantifies methane plumes globally. The system achieves 84% recall on expert-annotated plumes and provides a scalable solution for tracking point-source emissions. This effort supports the Global Methane Pledge to reduce methane emissions by 30% by 2030.

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Generative AI

Health & Bioscience

A large-scale study with 13,917 participants shows that Google's SymptomAI conversational agent can produce differential diagnoses that are often preferred by clinicians over those of other clinicians, and correlates with wearable biosignal data.

Google Quantum AI integrates reinforcement learning with quantum error correction to create a quantum computer that continuously adapts to drift and remains stable during long computations.

Google Research reveals that the creativity of diffusion models is a mathematical consequence of 'score smoothing' during neural network training, enabling interpolation between training data points.

Google Research introduces SensorFM, a foundation model for wearable health pretrained on over one trillion minutes of sensor data from five million people. It learns a general-purpose representation of human physiology that transfers across 35 health tasks, supports label-efficient adaptation, and can ground a Personal Health Agent.

Google Research conducted a large-scale real-world study in 10 US cities showing that slightly rerouting a small fraction of trips (under 2%) using navigation apps can measurably reduce traffic congestion and emissions. The study, published in Nature Cities, found median speed increases of 2% on targeted segments and potential CO2e savings of thousands of tons per city per year.

Google Research introduces TabFM, a foundation model for tabular data integrated into BigQuery ML that enables zero-shot classification and regression via in-context learning, eliminating manual hyperparameter tuning and feature engineering. Trained on millions of synthetic datasets, it outperforms tuned traditional algorithms on the TabArena benchmark.

Google researchers introduce a method to retrofit Multi-Token Prediction onto deployed Gemini Nano v3 models without retraining the backbone, achieving faster inference and lower energy consumption on mobile devices. Deployed on Pixel 9 and 10 series, it boosts speed by over 50% for features like AI Notification Summaries and Proofread.

Google researchers propose linear elastic caching, which models cache management as a ski rental problem, using lightweight machine learning to dynamically adjust cache size. In Spanner production, it reduced memory usage by 15.5%, TCO by ~5%, with only 5.5% more cache misses and negligible I/O impact.

Google Research reveals a counterintuitive phenomenon: even for simple factual questions, prompting LLMs to generate reasoning chains improves answer accuracy. Two mechanisms are identified: computational buffer (extra tokens provide additional computation) and factual priming (generating related facts facilitates retrieval).

Google Research presents two studies on dermatology AI tools. A large-scale survey found AI assistance tripled users' accuracy in naming skin conditions, but improving next-step decisions remains challenging. A qualitative community study showed the app helped users and clinicians, with 92% of clinicians finding it helpful.

Google researchers introduce Regularized f-Divergence Kernel Tests to audit machine unlearning and privacy. The framework adaptively selects optimal divergence measures, improving detection of data leaks and unlearning failures while requiring fewer samples and less tuning.

Google's new Agentic RAG framework uses multiple specialized agents to iteratively search and verify context before answering complex queries, achieving up to 34% higher accuracy than standard RAG.

Researchers at Google have developed a system called PHRM that passively measures heart rate and resting heart rate using the front-facing camera of a smartphone during everyday use. In a study published in Nature, the system achieved an accuracy of less than 10% mean absolute percentage error compared to ECG, and less than 5 bpm error for daily resting heart rate compared to a wearable. The system was tested on a diverse dataset of over 350,000 video clips from nearly 700 participants, ensuring balanced representation across skin tones. PHRM outperformed 15 leading remote photoplethysmography models and is the only model to meet accuracy standards for all skin tones in real-world conditions.

At Google I/O 2026, Google Research showcased breakthroughs in scientific discovery, health, edge computing, and weather prediction. Highlights include Gemini for Science (ERA, Co-Scientist), Google Health app, Symptom AI, AMIE, Coral NPU, and AI for extreme weather. These innovations demonstrate AI's potential to amplify human ingenuity.

Published today in Nature, ERA is an AI tool that uses Gemini to write and optimize scientific code, achieving expert-level performance across benchmarks. It helped build Computational Discovery, now available through a trusted tester program in Google Labs. Applications include epidemiology, hydrology, CO2 mapping, solar energy, and retail forecasting.