Large Language Models (LLMs)

Claude Opus 4.5, the latest version of Anthropic’s flagship model, extends the earlier version’s strengths in coding, computer use, and agentic workflows while generating fewer tokens.

Large language models can develop character traits like cheerfulness or sycophancy during fine-tuning. Researchers developed a method to identify, monitor, and control such traits.

Having recently revised its agreement with longtime partner OpenAI, Microsoft pledged to invest billions of dollars in Anthropic, one of OpenAI’s top competitors.

Large language models may have learned knowledge that’s relevant to a given prompt, but they don’t always recall it consistently. Fine-tuning a model to search its parameters as though it were searching the web can help it find knowledge in its own weights.

The latest open-weights large language model from Moonshot AI challenges top proprietary LLMs at agentic tasks by executing hundreds of tool calls sequentially and pausing to think between each.

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.