ColGraphRAG: Late-Interaction Evidence Retrieval for Multimodal GraphRAG
Quick Answer
The ColGraphRAG model enhances multimodal question answering by implementing late-interaction MaxSim-style scoring for graph-linked images, improving retrieval accuracy on MultimodalQA benchmarks.
Quick Take
This approach retains existing graph construction and reasoning processes while demonstrating significant gains in scenarios where visual evidence is critical.
Key Points
- Introduces late-interaction scoring for improved retrieval of graph-linked images.
- Maintains existing processes for graph construction and downstream reasoning.
- Demonstrates improved point estimates for visual evidence in MultimodalQA.
- Shows mixed trends on text-dominant questions, indicating nuanced performance.
- Highlights the need for further validation and graph-level diagnostics.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.16208 [cs.AI] |
| (or arXiv:2607.16208v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16208 arXiv-issued DOI via DataCite |
Submission history
From: Seonok Kim [view email]
[v1]
Sat, 9 May 2026 08:30:18 UTC (5,609 KB)
— Originally published at arxiv.org
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