Google has published a technical report for the Gemma 4 model family. The document is available on arXiv.
Google releases Gemma 4 technical report
Google releases TimesFM-3, a 330M parameter zero-shot multivariate time series foundation model
Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike previous univariate versions, it is pretrained natively for multivariate forecasting on more than 1 trillion time points and accepts multiple targets, past covariates, and past-future covariates without task-specific fine-tuning.
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.
Orthrus diffusion head trained Qwen 3.5/3.6 and Gemma 4 models dropping soon
The Orthrus project is preparing to release support for Qwen 3.5, Qwen 3.6, and Gemma 4 models using a diffusion head approach. The team has finalized testing and is currently setting up the release pipeline.
Ohio State University releases open-source Deep Research agent QUEST-35B
Ohio State University's NLP team has released QUEST-35B, an open-source Deep Research agent trained on approximately 32 H100 GPUs using 8,000 synthetic samples. The team open-sourced the training recipe, code, weights, and datasets, with benchmark results showing competitive performance compared to leading closed-source Deep Research systems.
Ohio State University releases open-source Deep Research agent QUEST-35B
Researchers at Ohio State University trained QUEST-35B, a Deep Research agent, using approximately 32 H100 GPUs and 8,000 synthetic samples. They open-sourced the training recipe, code, weights, and datasets, with benchmark results showing competitive performance compared to leading closed-source Deep Research systems.