UniversalRAG

UniversalRAG introduces a RAG system that handles knowledge retrieval from heterogeneous sources containing multiple data types (text, images, videos) with varying granularities. Rather than forcing diverse modalities into a single embedding space where embeddings cluster by modality rather than meaning, it uses modality-aware routing to dynamically select appropriate corpus and granularity for each query, outperforming both unimodal and unified multimodal RAG baselines across 10 benchmarks.