KV Cache Compression for Vision-Language Models

A new research paper on arXiv (2605.16439) introduces KVCapsule, a method for efficient sequential KV cache compression in Vision-Language Models (VLMs). VLMs extend Large Language Models (LLMs) to multimodal reasoning with text and image inputs, but suffer from high memory overhead due to large key-value caches during autoregressive decoding. Images produce longer token sequences and denser feature representations than text, and vision tokens exhibit structured attention patterns that render many LLM-oriented compression techniques ineffective. The authors conduct empirical analysis of vision token behavior and propose KVCapsule to address these challenges.