NaVLM-PVC: Progressive Visual Compression for Efficient Native-Resolution Encoding in MLLMs

Abstract

Visual encoding followed by token condensing has becomethe standard architectural paradigm in multi-modal large language mod-els (MLLMs). Many recent MLLMs increasingly favor global native-resolution visual encoding over slice-based methods. To investigate thistrend, we systematically compare their behavior on vision-language un-derstanding and attention patterns, revealing that global encoding en-hances overall capability but at the expense of greater computationaloverhead. To address this issue, we present NaVLM-PVC, an MLLM cen-tered upon our proposed Progressive Visual Compression (PVC) method,which can be seamlessly integrated into standard Vision Transformer(ViT) to enable efficient native-resolution encoding. The PVC approachconsists of two key modules: (i) refined patch embedding, which supportsflexible patch-size scaling for fine-grained visual modeling, (ii) windowedtoken compression, hierarchically deployed across ViT layers to progres-sively aggregate local token representations. Jointly modulated by thesetwo modules, a widely pretrained ViT can be reconfigured into an effi-cient architecture while largely preserving generality. Evaluated acrossextensive benchmarks, the transformed ViT, termed ViT-PVC, demon-strates competitive performance with MoonViT while reducing TTFT(time-to-first-token) by 2.4×, when developed within an identical MLLMarchitecture. Building upon ViT-PVC, NaVLM-PVC also achieves com-petitive performance to Qwen2-VL, while further reducing TTFT by1.9×. We will release all code and checkpoints to support future researchon efficient MLLMs.