The University of Maryland Applied Research Laboratory for Intelligence and Security (ARLIS), through its INSURE academic consortium, is supporting a research project to develop context-aware multimodal information retrieval systems – a next-generation capability that could transform how intelligence and security professionals access and analyze complex data.
The project, led by Dr. Alan McMillan at the University of Wisconsin and supported by Dr. Michael Brundage at ARLIS, focuses on a cutting-edge AI approach called Retrieval-Augmented Generation (RAG). RAG helps AI systems pull in new, up-to-date information during tasks, something standalone models can’t do. While RAG has mostly been used with text, this project aims to combine it with large multimodal models (LMMs) that can handle all types of data at once.