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This paper revisit VLM inference and presents a new efficient guidance scheme that complements similarity-based guidance, and proposes Cross Modal Residual (CMR), a training-free visual token compression method that combines CMR, text-attention relevance, and residual-space diversity to retain task-relevant and complementary tokens.
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