Machine Learning Research

AI systems designed to generate animated 3D scenes that include active human characters have been limited by a shortage of training data, such as matched 3D scenes and human motion-capture examples. Generated video clips can get the job done without motion capture.

Researchers updated the highly responsive Moshi voice-to-voice model to discuss visual input.

Diffusion transformers learn faster when they can look at embeddings generated by a pretrained model like DINOv2.

Diffusion models usually take many noise-removal steps to produce an image, which takes time at inference. There are ways to reduce the number of steps, but the resulting systems are less effective. Researchers devised a streamlined approach that doesn’t sacrifice output quality.

Google updated its open-weights family of large language models to include versions that handle image and video inputs.

Materials that have specific properties are essential to progress in critical technologies like solar cells and batteries. A machine learning model designs new materials to order.

An AI agent synthesizes novel scientific research hypotheses. It's already making an impact in biomedicine.

Multilingual AI models often suffer uneven performance across languages, especially in multimodal tasks. A pair of lean models counters this trend with consistent understanding of text and images across major languages.

Large language models built by developers in China may, in some applications, be less useful outside that country because they avoid topics its government deems politically sensitive. A developer fine-tuned DeepSeek-R1 to widen its scope without degrading its overall performance.

Microsoft debuted its first official large language model that responds to spoken input.

Most models that have learned to reason via reinforcement learning were huge models. A much smaller model now competes with them.

Anthropic’s Claude 3.7 Sonnet implements a hybrid reasoning approach that lets users decide how much thinking they want the model to do before it renders a response.

OpenAI launched GPT-4.5, which may be its last non-reasoning model.

Typical large language models are autoregressive, predicting the next token, one at a time, from left to right. A new model hones all text tokens at once.

Although large language models can improve their performance by generating a chain of thought (CoT) — intermediate text tokens that break down the process of responding to a prompt into a series of steps.