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Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers and rerankers optimize for semantic similarity rather than answer utility, creating a preference gap: documents that appear relevant may not help the generator produce a correct answer. Motivated by this, we propose a two-stage generator-in-the-loop alignment framework that closes this gap without human document-level relevance annotations. Our framework consists of two stages: in Stage 1, a VLM generates a hypothetical text passage from the i

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