M2N2 is a model merging technique that creates powerful multi-skilled agents without the high cost and data needs of retraining.

Memp takes inspiration from human cognition to give LLM agents "procedural memory" that can adapt to new tasks and environments.

Chain-of-Thought isn't a plug-and-play solution. For developers, this research offers a blueprint for LLM testing and strategic fine-tuning.

New research reveals open-source AI models use up to 10 times more computing resources than closed alternatives, potentially negating cost advantages for enterprise deployments.

New research reveals how OS agents — AI systems that control computers like humans — are rapidly advancing while raising serious security and privacy concerns.

A new study from Anthropic introduces "persona vectors," a technique for developers to monitor, predict and control unwanted LLM behaviors.

A common AI fine-tuning practice could be unintentionally poisoning your models with hidden biases and risks, a new Anthropic study warns.