Google has published a technical report for DiffusionGemma, available on arXiv. The document details the model's architecture and capabilities as part of the Gemma family.
Google releases DiffusionGemma technical report
Google open-sources WeatherNext models; Meta explores cross-modal synergy
Google has open-sourced its WeatherNext 2 and WeatherNext Cyclones AI models to improve cyclone forecasting accuracy. The models achieve state-of-the-art results in predicting track, intensity, and wind structure, providing forecasters with an average of one extra day of predictive accuracy compared to current methods.
Google DeepMind open sources WeatherNext AI model for cyclone forecasting
Google DeepMind and Google Research have open sourced the WeatherNext 2 and WeatherNext Cyclones AI models, which achieved state-of-the-art accuracy in predicting tropical cyclone tracks, intensity, and wind structure. Published in Nature, the work demonstrates that these models provide forecasters with an extra day of predictive accuracy compared to prior systems.
Microsoft releases Mage-Flow, a 4B native-resolution model for image generation and editing
Microsoft has released Mage-Flow, a compact 4B-scale generative stack designed for efficient text-to-image generation and instruction-based image editing. The system achieves state-of-the-art-competitive quality through the co-design of a lightweight tokenizer, Mage-VAE, and a Native-Resolution Multimodal Diffusion Transformer (NR-MMDiT).
Cactus post-trains Gemma 4 E2B with a probe to output confidence scores
Cactus has post-trained the Gemma 4 E2B model to generate a confidence score between 0 and 1 for every response, enabling developers to route uncertain queries to larger cloud models. The team achieved this by adding a lightweight 68k parameter probe layer that reads intermediate hidden states during decoding to predict the probability of an error.
Google releases Gemini 3.6 Flash; OpenAI reports model security escape
Google has released three new models: Gemini 3.6 Flash for efficient general-purpose agent workloads, 3.5 Flash-Lite for low-latency applications, and a cyber-specialized 3.5 Flash model integrated with CodeMender.