Machine Learning Research

Providers of large language models stand to benefit by building models that spur user engagement, but users may bear a cost in undue influence on their world views.

An AI agent proposed new medical uses for established drugs nearly autonomously — uses that were supported by experiments on isolated human cells — with human input only to name diseases to be treated and run the AI-proposed lab experiments. 

ChatGPT’s voice mode now listens and speaks at the same time, passing harder questions posed to the conversational model to a reasoning model in the background.

Imagine you are typing a sentence. But instead of a keyboard, joystick, or eye tracker, a device surrounding your head reads your intentions and generates that sentence on screen.

DeepSeek built a speculative decoding module that speeds up its production models’ text generation by more than 50 percent without sacrificing accuracy, then made its technique open source.

Google built a low-cost, high-throughput pipeline for developers working with media, combining Gemini’s fastest image model yet with a similarly speedy multimodal model that can turn images into video with synchronized sound.

When you’re training a robot via reinforcement learning, a handcrafted reward function is labor-intensive to build but often dispenses rewards more effectively than a general-purpose reward model based on a vision-language model. Researchers built reward models that narrowed the gap.

Microsoft, once OpenAI’s exclusive partner and still a major reseller of other companies’ AI models, built its own reasoning model from scratch.

Models that orchestrate the activities of other models and agents achieved state-of-the-art performance on a variety of benchmarks, outperforming the best individual models working alone.

OpenAI announced a preview of its GPT-5.6 family, including a top-tier model comparable to Claude 5 Mythos — but so far it’s available only to users that are selected by the U.S. government.

Anthropic mid-2026 update of Opus held the the top of a leading intelligence ranking for about a week, only to be overtaken by Claude Fable 5.

Google’s AlphaFold models pioneered the task of finding the shapes of biologically active molecules, opening new pathways for drug development.

The third generation of Apple Foundation Models — fruit of Apple’s collaboration with Google — introduces a variation on the mixture-of-experts architecture that runs on local devices. 

Z.ai released an open-weights model that rivals proprietary leaders for autonomous agentic tasks.

Reinforcement learning can’t train a model to solve a difficult problem if the model doesn’t discover all the right steps.