Paper page – UniversalRAG: Retrieval-Augmented Generation over Multiple Corpora with Diverse Modalities and Granularities

Retrieval-Augmented Generation (RAG) has shown substantial promise in
improving factual accuracy by grounding model responses with external knowledge
relevant to queries. However, most existing RAG approaches are limited to a
text-only corpus, and while recent efforts have extended RAG to other
modalities such as images and videos, they typically operate over a single
modality-specific corpus. In contrast, real-world queries vary widely in the
type of knowledge they require, which a single type of knowledge source cannot
address. To address this, we introduce UniversalRAG, a novel RAG framework
designed to retrieve and integrate knowledge from heterogeneous sources with
diverse modalities and granularities. Specifically, motivated by the
observation that forcing all modalities into a unified representation space
derived from a single combined corpus causes a modality gap, where the
retrieval tends to favor items from the same modality as the query, we propose
a modality-aware routing mechanism that dynamically identifies the most
appropriate modality-specific corpus and performs targeted retrieval within it.
Also, beyond modality, we organize each modality into multiple granularity
levels, enabling fine-tuned retrieval tailored to the complexity and scope of
the query. We validate UniversalRAG on 8 benchmarks spanning multiple
modalities, showing its superiority over modality-specific and unified
baselines.