Gemini 3.1 Flash Live: Making audio AI more natural and reliable
Our latest voice model has improved precision and lower latency to make voice interactions more fluid, natural and precise.
More from the blog
Paving the way for greener ammonia production
New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.
Same Cluster, 33 Points More Utilization: What Changed Was the Order
A Blog post by Dharma-AI on Hugging Face
Introducing Gemini 3.7 Flash
Gemini 3.7 Flash is our most intelligent workhorse model yet for coding and agents.
Advancing AMIE towards expert-level audio-visual clinical consultations
Health & Bioscience
Related papers
Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence
Mechanist is an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI intelligence, and develops a mechanism theory of belief, revealing how models represent world knowledge, form beliefs, infer the beliefs of others, and how these mechanisms emerge during pretraining.
Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis
This paper investigates how multilingual medical adaptation reshapes the internal representations of Whisper models through layer-wise encoder analysis, and shows that English medical fine-tuning produces the dominant encoder shift, whereas multilingual continuation largely preserves the adapted representation space.
ComponentBench: Diagnosing Component-Level Failures in Computer-Use Agents
This work presents ComponentBench, a benchmark and diagnostic pipeline for component-level evaluation of computer-use agents on modern web UIs, and introduces a scalable pipeline for auditing realized structural difficulty after implementation and synthesizing structured failure analyses across tasks and component families.
Same Facts, Different Updates: Inference Setup Shapes LLM Behavior in Medical Allocation
This work studies a medical example in which a model is asked to assign resource-allocation probabilities to two people given brief clinical context, and then sees the same scenario with a single extra sentence containing contrasting patient information, showing the context-dependent effect of patient information in a sensitive medical use case.