Title: LLaVA-UHD v3: Progressive Visual Compression for Efficient Naive-Resolution Encoding in MLLMs
Primary area: foundation or frontier models, including LLMs
Keywords: Multimodal Large Language Model

Abstract: Visual encoding followed by token condensing has become the standard architectural paradigm in multi-modal large language models (MLLMs). Many recent MLLMs increasingly favor global naive-resolution visual encoding over slice-based methods. 

To investigate this trend, we systematically compare their behavior on vision-language understanding and attention patterns, revealing that global encoding enhances overall capability but at the expense of greater computational overhead.
To address this issue, we present LLaVA-UHD v3, an MLLM centered upon our proposed Progressive Visual Compression (PVC) method, which can be seamlessly integrated into standard Vision Transformer (ViT) to enable efficient naive-resolution encoding.

The PVC approach consists of two key modules: (i) refined patch embedding, which supports flexible patch-size scaling for fine-grained visual modeling, (ii) windowed token compression, hierarchically deployed across ViT layers to progressively aggregate local token representations. 

Jointly modulated by these two modules, a widely pretrained ViT can be reconfigured into an efficient architecture while largely preserving generality.

Evaluated across extensive benchmarks, the transformed ViT, termed ViT-UHD, demonstrates competitive performance with MoonViT while reducing TTFT (time-to-first-token) by 2.4$\times$, when developed within an identical MLLM architecture. Building upon ViT-UHD, LLaVA-UHD v3 also achieves competitive performance to Qwen2-VL, while further reducing TTFT by 1.9$\times$.We will release all code and checkpoints to support future research on efficient MLLMs.

TL;DR: We introduce LLaVA-UHD v3, which achieves competitive performance with state-of-the-art MLLMs. With Progressive Visual Compression inside ViT, ViT-UHD improves efficiency by 2.4×, and LLaVA-UHD v3 reduces inference latency by 1.9×.