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DoPR slashes online reranking costs by reusing compressed document prefixes, achieving remarkable efficiency without sacrificing performance.
Fine-tuning just 0.14% of parameters, ENCORE boosts VLM accuracy by 1.43% through innovative entropy-guided cropping and attention techniques.
Pruning visual tokens based on head alignment can retain nearly all performance while drastically reducing computational costs.
Allocating rollout budgets based on state informativeness allows LLM agents to achieve superior performance in complex decision-making tasks without increasing computational costs.
Forget fine-tuning: expert-guided LLM agents can unlock vast troves of scientific data buried in unstructured papers with surprising accuracy.
Forget hand-tuning: VisPCO automatically finds optimal visual token pruning configurations in VLMs, outperforming predefined strategies across diverse benchmarks.
