MedSTP: Preserving Subspace and Transition Evidence for Medical VQA Token Pruning

Medical vision-language models (VLMs) for visual question answering (VQA) often pass long visual-token sequences to the language model, making visual-token pruning attractive but risky for medical images, where answers may depend on localized boundary and texture evidence.