GQR is a test-time optimization technique that refines primary retriever query embeddings using complementary retriever scores to achieve high performance with smaller representations in multimodal visual document retrieval.
Antoine Chaffin and Aur´elien Lac
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Agentic search over NASA EO-KG yields a 47k-pair benchmark where neural scoring plus LLM reranking raises MRR by over 5x then an additional 28%.
IRAP quantifies ambiguous performance requirements into mathematical functions via interactive retrieval-augmented preference elicitation and outperforms ten prior methods on four real-world datasets with up to 40x gains in five interaction rounds.
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Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization
GQR is a test-time optimization technique that refines primary retriever query embeddings using complementary retriever scores to achieve high performance with smaller representations in multimodal visual document retrieval.