Large Language Models (LLMs)

When processing long contexts, large language models often lose track of details or devolve into nonsense. Researchers reduced these effects by managing context externally.

Nvidia, the dominant supplier of AI chips, released a competitive open-source large language model whose speed tops its size class — the first open-weights leader to come from the United States since last year, when Meta delivered Llama 4.

Downloads of mobile AI apps and resulting revenue are surging.

OpenAI updated its flagship models, extending the ability to use tools and setting the state of the art on a handful of benchmarks, and priced them at the top of the market. Its coding and agentic abilities have enabled Codex, OpenAI’s competitor to Anthropic’s Claude Code, to leap ahead.

LLMs have achieved gold-medal performance in math competitions. An agentic system showed strength in mathematical research as well.

Projected demand for output from large language models is spurring a massive buildout of data centers. Researchers asked whether smaller models running on local devices could meaningfully lighten that load.

Makers of software that runs large companies saw their share prices plunge as investors worried that AI systems could undermine their businesses. This week, their stocks rebounded somewhat as Anthropic partnered with some of the same companies.

Reasoning models in the 1 to 2 billion-parameter range typically require more than 1 gigabyte of RAM to run. Liquid AI released one that runs in less than 900 megabytes, and does it with exceptional speed and efficiency.

Z.ai more than doubled the size of its flagship large language model to deliver outstanding performance among open-weights competitors.

Elon Musk’s SpaceX acquired xAI, opening the door to richer financing of the merged entity’s AI research, a tighter focus on space applications of AI, and — if Musk’s dreams are realized — solar-powered data centers in space.

Anthropic updated its flagship large language model to handle longer, more complex agentic tasks.

On its 25th anniversary, Wikipedia celebrated with high-profile deals to make its data easier for AI companies to train their models in exchange for financial support.

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.

Apple cut a multi-year deal with Google to use Gemini models as the basis of AI models that reside on Apple devices.