miniReranker reduces multimodal reranking runtime to under 1% of the dense baseline under high-reuse conditions while retaining over 96% of performance via vision-first prompting, early exit, sparse cross-segment attention, and embedder-guided token pruning.
VLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training
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miniReranker: Efficient Multimodal Reranking through Visual Cache Reuse and Interaction Sparsity
miniReranker reduces multimodal reranking runtime to under 1% of the dense baseline under high-reuse conditions while retaining over 96% of performance via vision-first prompting, early exit, sparse cross-segment attention, and embedder-guided token pruning.