HiPP-Prune: Hierarchical Preference-Conditioned Structured Pruning for Vision-Language Models

ArXi:2603.06270v1 Announce Type: cross Pruning vision-language models (VLMs) for efficient deployment is challenging because compression can affect not only task utility but also visual grounding, often amplifying object hallucinations even at the same sparsity level. We present HiPP-Prune, a hierarchical preference-conditioned structured pruning framework that treats pruning as conditional resource allocation under multiple objectives.