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DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models
Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redundancy or text-vision attention. However, they frequently suffer from semantic degradation due to their task-agnostic design or unreliable attention es
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