Benchmarks

Google introduced Gemini 3 Pro and Nano Banana Pro, its flagship vision-language and image-generation models, and deployed them to billions of users worldwide.

Transformers are well suited to predicting future values of time series like energy prices, wages, or weather, but often — as in those examples — multiple time series often influence one another. Researchers built a model that can forecast multiple time series simultaneously.

A new type of transformer can check its work. Instead of guessing the next output token in one shot like a typical transformer, it starts with a rough version of the token and improves it step by step.

A bottleneck in fine-tuning large language models for software engineering is building a dataset that can show them how to edit code, search for subroutines, write test scripts, control a terminal, manage a file system, and so on. Researchers built a pipeline that produces such data automatically.

An independent AI test lab detailed the rising cost of benchmarking reasoning models.

Using an 8-bit number format like FP8 during training saves computation compared to 16- or 32-bit formats, but it can yield less-accurate results. Researchers trained models using 4-bit numbers without sacrificing accuracy.

Large language models excel at processing text but can’t interpret images, video, or audio directly without further training on those media types. Researchers devised a way to overcome this limitation.

OpenAI refreshed its roster of models and scheduled the largest, most costly one for removal.

Google’s new flagship model raised the state of the art in a variety of subjective and objective tests.

If you have a collection of variables that represent, say, a cancer patient and you want to classify the patient’s illness as likely cancer or not, algorithms based on decision trees, such as gradient-boosted trees, typically perform better than neural networks.

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.

Merging multiple fine-tuned models is a less expensive alternative to hosting multiple specialized models. But, while model merging can deliver higher average performance across several tasks, it often results in lower performance on specific tasks. New work addresses this issue.

OpenAI launched not only its highly anticipated o1 model but also an operating mode that enables the model to deliver higher performance — at a hefty price.

Coding agents are improving, but can they tackle machine learning tasks?