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S$^2$Prune: Spatially Structured Visual Token Pruning for Multimodal Large Language Models
Visual token pruning reduces the inference overhead of multimodal large language models (MLLMs) by retaining only a subset of visual tokens. Existing methods usually select tokens based on importance or redundancy. However, we observe that these criteria produce stable spatial biases across inputs a
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