Multimodal Agentic RAG is not possible - even w/ MCP
AI Summary
The video titled “Multimodal Agentic RAG is not possible - even w/ MCP” discusses new research from the Georgia Institute of Technology on the limitations of In-Context Learning (ICL) in Retrieval-Augmented Generation (RAG) systems. It highlights the essential role of Multi-Modal In-Context Learning (MM-ICL) for enhancing the functionality of multimodal models, particularly in reasoning over retrieved data. The video explains how various capabilities of RAG systems are compromised if ICL is ineffective, essentially reducing them to basic summarization tools. It asserts that current models fail to learn effectively, relying instead on mimicking shallow patterns, leading to flawed generation despite perfect retrieval processes. This insights aim to inform future advancements in multimodal AI systems.