Large Language Models (LLMs)

Large language models often memorize details in their training data, including private information that may appear only once, like a person’s name, address, or phone number. Researchers built the first open-weights language model that’s guaranteed not to remember such facts.

An open-weights model from Shanghai-based MiniMax challenges top proprietary models on key benchmarks for coding and agentic tasks.

For decades, AI developers have treated the web as an open faucet of training data. Now publishers are shutting the tap. Will web data dry up?

Conversations with chatbots are loosening users’ grips on reality, fueling the sorts of delusions that can trigger episodes of severe mental illness. Are AI models driving us insane?

Honing an agent’s prompt can yield better results than fine-tuning the underlying large language model via reinforcement learning.

The ability to easily connect large language models to tools and data sources has made Model Context Protocol popular among developers, but it also opens security holes, research shows.

Reasoning models typically learn to undertake a separate process of “thinking” through their output of before they produce final response. Ant Group built a top non-reasoning model that can take similar steps as part of its immediate response.

The first offering from Thinking Machines Lab, the startup founded by former OpenAI CTO Mira Murati, aims to simplify — and democratize — the process of fine-tuning AI models.

DeepSeek’s latest large language model can cut inference costs by more than half and processes long contexts dramatically faster relative to its predecessor.

The approach known as LoRA streamlines fine-tuning by training a small adapter that modifies a pretrained model’s weights at inference. Researchers built a model that generates such adapters directly.

Alibaba rounded out the Qwen3 family with its biggest large language model to date as well as smaller models that process text, images, video, and/or audio.

OpenAI and Meta, which have been content to offer standalone chatbots or tuck them into existing products, introduced dueling social video networks and other initiatives designed to boost revenue and engagement.

Anthropic updated its mid-size Claude Sonnet model, making it the first member of the Claude family to reach version 4.5. It also enhanced the Claude Code agentic coding tool with long-desired features.

Fine-tuning large language models via reinforcement learning is computationally expensive, but researchers found a way to streamline the process.

What do ChatGPT’s 700 million weekly active users do with it? OpenAI teamed up with a Harvard economist to find out.