Recent advancements in Large Language Model (LLM) agents allow for complex workflows where models autonomously retrieve information, utilize tools, and reason over large datasets. Retrieval-augmented generation (RAG) is increasingly adopted in agentic search systems to enhance task completion.

A grammar-based method called LambdaG can match or outperform advanced AI systems in identifying text authorship. This method utilizes patterns in grammar and sentence construction, providing comparable accuracy with greater transparency and lower computational costs.