V-Skip applies block-wise structured sparsity to skip saturated visual self-attention in deeper MLLM layers while retaining FFNs, using few-shot calibration for task-specific paths and achieving 94.16-100.31% performance retention.
How visual representations map to language feature space in multimodal llms.CoRR, abs/2506.11976, 2025
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Look Less, Reason More: Block-wise Attention Skipping for Efficient Multimodal LLMs
V-Skip applies block-wise structured sparsity to skip saturated visual self-attention in deeper MLLM layers while retaining FFNs, using few-shot calibration for task-specific paths and achieving 94.16-100.31% performance retention.