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RaDiCal reveals that relying solely on attention saliency can misguide token pruning, leading to a 39-45% reduction in FLOPs while enhancing ranking performance across key datasets.
Transforming retrieval failures into hard pairs can boost model performance while using 26% to 75% fewer synthetic samples than conventional methods.
Image corruptions can drastically impair retrieval performance, revealing that clean image-only queries often outperform joint retrieval in snap-and-ask scenarios.
EviRank redefines multimodal image re-ranking by transforming complex queries into structured evidence, achieving unprecedented accuracy and efficiency.
LedgerMind reveals that grounding multimodal reasoning in a structured evidence ledger can significantly mitigate common pitfalls like entity hallucination and unsupported reasoning.
