ColGraphRAG: Late-Interaction Evidence Retrieval for Multimodal GraphRAG

Abstract:Graph-grounded multimodal question answering organizes text, tables, and images in a structured evidence graph, yet end-to-end accuracy depends on which multimodal assets are ranked highly enough to enter downstream reasoning; for graph-linked images, single-vector bi-encoder similarity can discard patch- and token-level structure needed for fine-grained alignment. We evaluate replacing the visual candidate-ranking operator over graph-linked image nodes with late-interaction MaxSim-style multi-vector scoring in the ColBERT/ColPali lineage, while keeping offline graph construction, text- and table-side retrieval, structured extraction, and downstream reasoning unchanged. On MultimodalQA, this change is associated with improved retrieval-stage point estimates for graph-linked image candidates and downstream QA gains, with larger movement where visual evidence matters most and mixed trends on text-dominant questions; we interpret the pattern as mechanism-level evidence for graph-linked visual evidence inclusion, while broader validation and finer graph-level diagnostics remain important future work.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2607.16208 [cs.AI]

(or arXiv:2607.16208v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.16208 Focus to learn more arXiv-issued DOI via DataCite

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