SAGE mixes attention importance with embedding diversity sampling to reach 93% of full server accuracy on ImageNet-1K while sending under half the evidence units, beating pure importance selection.
AgilePruner: An empirical study of attention and diversity for adaptive visual token pruning in large vision-language models,
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SAGE: Training-Free Semantic Evidence Composition for Edge-Cloud Inference under Hard Uplink Budgets
SAGE mixes attention importance with embedding diversity sampling to reach 93% of full server accuracy on ImageNet-1K while sending under half the evidence units, beating pure importance selection.