ApET: Approximation-Error Guided Token Compression for Efficient VLMs

arXiv:2602.19870v1 Announce Type: new Abstract: Recent Vision-Language Models (VLMs) have demonstrated remarkable multimodal understanding capabilities, yet the redundant visual tokens incur prohibitive computational overhead and degrade inference efficiency. Prior studies typically relies on [CLS]…

View Post on Scour ↗

More from arxiv.org on Scour.