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
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
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
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
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.