Agentic RAR AI Framework by Oxford University

In a recent presentation by Discover AI, researchers from the University of Oxford introduced an advanced reasoning framework called Agentic RAR (Reasoning on Agentic Reasoning) that enhances traditional Retrieval Augmented Generation (RAG) systems. The new model emphasizes the use of multiple specialized agents, including code agents and search agents, to perform complex reasoning tasks in real-time. The presentation highlights how these agents can collaboratively analyze data and provide insights for decision-making, particularly in finance. The researchers argue that this multi-agent approach leads to improved performance in reasoning tasks compared to classical RAG systems. The video concludes with a discussion on the potential of this framework to revolutionize AI-driven research and decision-making processes.