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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.
Despite enhancements in classification accuracy, the fundamental ranking gap in Chain-of-Thought models remains a stubborn bottleneck in pointwise reranking.
Transforming retrieval failures into hard pairs can boost model performance while using 26% to 75% fewer synthetic samples than conventional methods.
EviRank redefines multimodal image re-ranking by transforming complex queries into structured evidence, achieving unprecedented accuracy and efficiency.
Search-oriented rubrics redefine how we evaluate document sets, leading to a 2.6-point performance boost in deep research tasks.
