Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion

arXiv:2609.19417 · cs.CL · Submitted 2026-09-16 · Read on arXiv

cs.CL

Submitted: 2026-09-16

Updated: 2026-09-16

Code: https://github.com/hk-zh/TrioRAG

License: http://creativecommons.org/licenses/by/4.0/

The gist: Graph-based retrieval-augmented generation (RAG) is widely used for multimodal, cross-document question answering.

Terminology

Abstract

Graph-based retrieval-augmented generation (RAG) is widely used for multimodal, cross-document question answering. However, building corpus-level graphs is expensive, slow to query, and difficult to maintain. We present TrioRAG, a graph-free multimodal framework that integrates evidence from three complementary signals: the question, the anchor image, and a VLM-enhanced query generated from both. Each signal retrieves independently over a shared multi-vector index of page text and page images, and the results are combined through late fusion. Further, we introduce AutoQA, a multimodal automotive benchmark whose questions are grounded in noisy, web-sourced images rather than clean document-sourced figures. Its questions require reasoning across manuals. We position it as a model-curated testbed rather than a human-validated gold standard. Across three benchmarks, TrioRAG matches or outperforms graph-based systems while reducing total cost and accelerating per-query inference by 1.6-2.3 times. By construction, AutoQA grounds its questions in out-of-corpus web images. In this setting image retrieval reaches only 19.3% document-level recall, while text-derived signals, especially the VLM-enhanced query, keep retrieval robust.

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