Index-Preserving Lightweight Token Pruning for Efficient Document Understanding in Vision-Language Models

Researchers have developed a lightweight token pruning framework that reduces computational costs for vision-language models in document understanding tasks by filtering out non-informative background regions before processing. The approach uses a binary patch-level classifier and max-pooling refinement to maintain accuracy while substantially lowering compute demands.