A Better Newspaper

## Overview Retrieval-Augmented Generation (RAG) has become a standard architecture for grounding LLM outputs in external knowledge. Most production RAG systems are limited to text corpora, but research is rapidly expanding RAG to multimodal settings. ## The Modality Gap in RAG Existing RAG approaches predominantly operate over single-modality corpora—either text, images, or video—limiting their utility for real-world queries that require heterogeneous knowledge types (arXiv:2504.20734). A query about a technical process may require text documentation, engineering diagrams, and video demonstrations; single-modality RAG cannot serve all three simultaneously. ## UniversalRAG UniversalRAG proposes a unified retrieval framework operating over corpora of diverse modalities and granularities (arXiv:2504.20734). The system routes queries to appropriate modality-specific corpora and integrates retrieved content across modalities before generation. This reportedly improves factual accuracy on queries requiring cross-modal knowledge compared to modality-specific baselines. ## Interaction with Group-Robust Structured Prediction STaR-DRO addresses structured prediction under label imbalance and heterogeneous group difficulty, combining ontology-constrained generation with Tsallis reweighting (arXiv:2604.09737). This is relevant to RAG systems that must generate structured outputs (e.g., medical coding, legal citation) grounded in retrieved evidence while maintaining accuracy across underrepresented categories. ## Strategic Relevance - **Enterprise knowledge management**: Multimodal RAG enables grounding LLM responses in the full diversity of organizational knowledge assets (documents, images, video, data). - **Legal and compliance**: RAG architectures that can retrieve and cite specific source documents are essential for defensible AI-assisted legal research. - **Vendor evaluation**: Organizations procuring RAG infrastructure should assess multimodal retrieval capability as a near-term requirement.