Machine Learning Research

Mistral compressed Mistral Small 3.1 into much smaller versions, yielding a family of relatively small, open-weights, vision-language models that perform better by some measures than competing models of similar size. The method combines pruning and distillation.

An open source vision-language model unleashes minion agents that enable it to perform tasks more quickly and effectively.

The OpenClaw open-source AI agent became a sudden sensation, inspiring excitement, worry, and hype about the agentic future.

Individuals and organizations increasingly use large language models to produce media that helps them compete for attention. Does fine-tuning LLMs to encourage engagement, purchases, or votes affect their alignment with social values? Researchers found that it does.

Artificial Analysis, which tests AI systems, updated the component evaluations in its Intelligence Index to better reflect large language models’ performance in real-world use cases.

Image generators often mangle text. An open-weights model outperforms open and proprietary competitors in text rendering.

Current methods that produce 3D scenes from text or images are slow and produce inconsistent results. Researchers introduced a technique that generates detailed, coherent 3D scenes seconds.

Chain-of-thought reasoning can help autonomous vehicles decide what to do next.

Can your retriever find all the relevant documents for any query your users might enter? Maybe not, research shows.

One way to improve a reasoning model’s performance is to let it produce a longer chain of thought. However, attending to ever-longer contexts can become expensive, and making that attention more efficient requires changes to a model’s architecture.

An open protocol aims to enable AI agents to conduct scientific research autonomously across disciplinary and institutional boundaries.

Large language models occasionally conceal their failures to comply with constraints they’ve been trained or prompted to observe. Researchers trained an LLM to admit when it disobeyed.

Next year, I’m excited to see AI break out of 1:1 relationships with each of us. In 2026, AI has the potential to bring people together and unite us with human connection, rather than polarize and isolate us. It’s about time for ChatGPT to enter your group chats.

Over the past few years, we have seen rapid progress in models that jointly reason over text, images, sequences, graphs, and time series. Yet in biomedical settings, these capabilities often remain fragmented, brittle, or difficult to interpret.

AI research in 2026 should confront a simple but transformative realization: Models that predict are not the same as systems that act. The latter is what we actually need.