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A New Era of Innovation: Google Research at I/O 2026
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1. Home 2. Blog A New Era of Innovation: Google Research at I/O 2026
May 28, 2026
Yossi Matias, Vice President, Google & GM, Google Research
At Google I/O 2026 last week, Google teams showcased our most advanced technologies for users, developers and researchers. Here are some highlights from Google Research this year, often tapping into years-long efforts to realize the magic cycle of research. Quick links Share [](https://twitter.com/intent/tweet?text=https%3A//research.google/blog/a-new-era-of-innovation-google-research-at-io-2026/ "Share on Twitter") [](https://www.facebook.com/sharer/sharer.php?u=https%3A//research.google/blog/a-new-era-of-innovation-google-research-at-io-2026/ "Share on Facebook") [](https://www.linkedin.com/shareArticle?url=https%3A//research.google/blog/a-new-era-of-innovation-google-research-at-io-2026/&mini=true "Share on LinkedIn") [](mailto:name@example.com?subject=Check%20out%20this%20site&body=Check%20out%20https%3A//research.google/blog/a-new-era-of-innovation-google-research-at-io-2026/ "Send via Email") Copy link ×
This year’s breakthroughs at Google I/O reflect a new bold agentic era. With models that are more powerful than ever and an agentic coding platform, we’re making Google products substantially more helpful for everyone while transforming how researchers tackle the most pressing scientific and societal challenges. As research translates into tangible, real-world impact, we’re turning AI and technology into an amplifier of human ingenuity.
Here are a few key highlights from Google Research, done in close collaboration with many teams across Google and global partners. Towards a new era of scientific discovery
AI is enabling a new era of scientific discovery. Google is building advanced AI-based tools designed to accelerate progress for the global scientific community. Our foundational technology is empowering researchers worldwide to drive breakthroughs across domains using the scientific method from hypothesis generation to computational experimentation. At I/O, we announced Gemini for Science which is built with our foundational research, including Empirical Research Assistance (ERA) and Co-Scientist — both published in _Nature_ last week.
Empirical Research Assistance (ERA) is a research coding system developed to help scientists write expert-level empirical software. Last week’s ERA publication in _Nature_ followed months of collaboration with academic partners to explore the system’s real-world applications. ERA has helped accelerate discoveries from neuroscience to cosmology. Our latest results include predicting hospital admissions for respiratory illnesses and forecasting seasonal runoff across California's river basins. These are available in our new GitHub directory. They signal the power of AI to unlock deeper insights with compute and accelerate discovery.
_Given a well-defined problem and a scoring system, Empirical Research Assistance (ERA) acts as a code-optimizing research engine. ERA proposes new concepts, writes code and evaluates the results. It then searches and iterates through thousands of code variants, using tree search to optimize performance._
Co-Scientist is a multi-agent system based on Gemini which works as a collaborative AI partner. Our foundational research on Co-Scientist was published last week in _Nature_ along with a blog highlighting testimonials from researchers. Our previous research and validation00973-0)papers demonstrate how researchers are using Co-Scientist to tackle some of the most pressing scientific challenges, from antimicrobial resistance00973-0) to plant immunity and liver fibrosis.
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_An overview of the Co-scientist. It uses a coalition of specialized agents who iteratively generate, evaluate, and refine hypotheses._
Gemini for Science is a suite of experimental tools designed to expand the scale and precision of scientific exploration — developed in close collaboration with teams from Google Cloud, Google DeepMind and Google Labs. One of the new tools in Gemini for Science, Computational Discovery, is an agentic research engine built with ERA and AlphaEvolve. The Computational Discovery prototype generates and scores thousands of code variations in parallel, enabling scientists to rapidly test multiple hypotheses and novel modeling approaches that would take months to explore manually.
With millions of papers published annually, synthesizing all the scientific literature has become a monumental challenge. Another new tool, Hypothesis Generation, was built using Co-Scientist. It aims to bridge this gap by collaborating with scientists to define a research challenge and running a multi-agent “idea tournament” to generate, debate and evaluate hypotheses. To ensure scientific rigor, claims are supported by clickable citations.
Gemini for Science also features Literature Insights, built with NotebookLM, which helps synthesize findings across scientific literature and structure the results. Plus, anyone engaged in agentic coding on platforms like Google Antigravity could benefit from the Science Skills, a collection of agent skills that automatically allow researchers to perform complex workflows like structural bioinformatics and genomic analyses in minutes rather than hours.
We are gradually opening access to these tools and partnering with the global scientific community to responsibly advance science. To register your interest, visit labs.google/science.
As part of our broader efforts to work with the ecosystem and foster access to our latest experiments, we’re also piloting tools for agentic peer review and scientific validation. Leading scientific conferences like ICML, STOC and NeurIPS are exploring our Paper Assistant Tool (PAT). Across these venues, PAT reviewed over 10,000 papers in an experimental capacity — helping many authors identify critical theoretical gaps or run entirely new experiments based on the AI tool's feedback.
We’re also accelerating mathematical and scientific discovery with Gemini Deep Think with advanced agentic reasoning. In collaboration with mathematicians, physicists, and computer scientists, we recently solved expert-level open research problems, including previously unsolved deadlocks in networks puzzles, settling a decade-old optimization conjecture, explaining machine learning optimization anomalies, upgrading economic theory for auctions, and resolving physics singularities in cosmic strings.
In the hands of scientists and researchers, these new types of AI based technologies could change how research is done and catalyze a new era of discoveries. Advancing Health with AI
AI can be instrumental in helping people live longer, healthier lives. For years, we’ve been advancing AI research to address healthcare challenges, working closely with healthcare providers, scientists, public officials and academics to bring our clinical research to real-world care settings and ensure that our innovations are safe and helpful.
One area we’ve been researching is how AI can best support people throughout their health and wellness journeys, from learning about symptoms and preparing for a doctor’s visit, to making sense of their medical records — a journey that starts before people ever see a doctor and extends long after. Our foundational research is enabling the new Google Health app and the Google Health Coach. Last week, we began the rollout of Google Health app to all existing Fitbit users, enabling eligible users to have personalized, holistic, adaptive coaching.
This builds on our multi-year research effort, including research on how a personal health LLM could help with sleep and fitness. Our latest research includes Symptom AI, an investigational tool designed to study how AI can help reason about conversational data salient to a user’s symptoms. In a randomized consented research study via the Fitbit app, 13,917 participants interacted with experimental AI agents, capturing real world diverse communication styles and a realistic distribution of illnesses. In a blind comparison on a cohort of study participants, independent clinicians reviewed the same conversations and preferred Symptom AI’s differential diagnoses about twice as often as those from other clinicians. In our Plan for Care pilot research study, we examined how 1,779 participants used our system to prepare for their doctor's visit. When compared with baseline models, 15% more users felt better prepared and 13% more users felt confident that they could make the most of their visit. In our Personal Health Record (PHR) research, we evaluated the impact of PHR data in model context on answer quality and found that both auto-raters and clinicians judged the AI responses to be significantly more helpful.
_Results from AI and clinician raters for helpfulness of AI responses when provided PHR context. Note that the raters (both AI and clinicians) are ‘oracles’ with access to the simulated user’…