Google Research at I/O 2026

Improving core capabilities in Gemini

We keep advancing foundational research for generative AI. In collaboration with Google DeepMind, our work in areas spanning factuality, multilinguality and efficiency helps to advance Gemini model quality and performance, and expand global access to our products, to better meet the needs of users.

Our research on LLM factuality goes back to pioneering research on evaluating factual consistency in 2021 and an early benchmark in 2022. We continue to push Gemini and AI Mode forward, and publish cutting edge research to help the entire community provide factual information. We’ve published FACTS and extended it to allow robust benchmarking of factuality in LLMs, and techniques to improve factuality, including text-to-image, video generation, long-context and expressions of uncertainty.

At I/O, we saw that information journeys are becoming increasingly complex, where people engage in longer conversations to obtain what they need. This creates several challenges for LLMs, including being able to reason and analyze more relevant information in the context window, adhering to constraints that appeared early in the conversation, and using longer reinforcement learning trajectories. Google Research has pioneered work on all these challenges, and these advances fuel our Gemini models.

The new Ask Maps feature also allows people to ask complex, longer questions in Google Maps. We partnered with Ask Maps to upgrade its evaluation framework and redefine how map helpfulness is measured. By pinpointing complex edge cases involving model reasoning and tool execution, this collaboration established a vital feedback loop — critical for continuous improvement of Ask Maps’ performance. We also drove research to improve the quality of Ask YouTube, a new feature which helps users find videos and information easily.

Generative AI is making tools and products far more accessible, and allowing technologies to finally meet users where they are. We’ve advanced multilinguality and localization capabilities for Gemini, including the publication of a benchmark which shows how LLMs operate in different languages, and in different locations, and open sourcing data in African languages, developed with the community. Our efforts helped enable the expansion of Gemini to more than 70 languages across more than 230 countries. This makes Gemini the most widely available AI assistant in the world.

Google builds its infrastructure to achieve low latency and high throughput, so that we can serve the needs of users, developers and enterprises around the world. Our research teams developed new techniques building on speculative decoding — including block verification and tree-structured drafting, which intelligently explores multiple candidate continuations at once and accepts more tokens per step. Our implementation is highly optimized for Google’s TPU architecture, maximizing hardware utilization to deliver substantially faster responses with no loss in quality. This work enabled the current speed of Gemini 3.5 Flash, with the same models also powering Antigravity and AI Studio.