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