Introducing computer use in Gemini 3.5 Flash
Get smart on it
Computer use is now built into Gemini 3.5 Flash, an AI model that previously only had this capability as a separate tool. This integrated feature allows developers to build agents that can see, reason, and take action across browser, mobile, and desktop environments, which enables improved performance for long-horizon tasks like continuous software testing and knowledge work across professional applications. To address security risks, the model uses targeted adversarial training and optional safeguards that require explicit user confirmation for sensitive actions and can stop tasks if prompt injection attacks are detected. Developers can access this capability through the Gemini API and Gemini Enterprise Agent Platform.
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DiScoFormer: One transformer for density and score, across distributions
DiScoFormer is a transformer model that estimates both the density and score of a data distribution in a single forward pass without requiring retraining for new distributions. The density describes where data points cluster, while the score, the gradient of log-density, points toward more probable regions and is used in diffusion-based generative models, Bayesian sampling, and scientific simulations. Existing methods force a trade-off: classical kernel density estimation works on any distribution but loses accuracy in high dimensions, while neural score-matching models stay accurate in high dimensions but must be retrained for each new distribution. DiScoFormer significantly outperforms kernel density estimation, cutting score error by 6.5 times and density error by more than 37 times in 100 dimensions, while generalizing to distributions with shapes and complexities not seen during training.
