Representation Dynamics Reveal Semantic Saliency and Similarity for Visual Token Pruning in MLLMs

Multimodal large language models (MLLMs) incur high inference latency from long visual token sequences. Existing pruning methods commonly use attention maps or output features to estimate token importance or redundancy. Several recent approaches also exploit representation changes, but when and how