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Binarization offers an attractive solution by drastically reducing storage and computational costs. However, existing binarization methods neglect the varying importance of weights across different layers and modalities. This causes parameters irrelevant to downstream tasks to be unnecessarily retained, whereas modality-critical weights may not be adequately optimized, resulting in significant performance degradation. To address these challenges, we develop a novel \\underline{S}ignificance-\\underline{A}ware \\underline{B}inarization for \\underline{L}arge \\underline{V}ision-\\underline{L}anguage \\underline{M}odels (SAB-LVLM). Specifically, after constructing He","title":"SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models","url":"https://arxiv.org/abs/2607.01876","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.01876v1 Announce Type: cross \nAbstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal understanding, yet their enormous parameter scale and cross-modal computation incur substantial memory and latency overhead, severely limiting real-world deployment on resource-constrained devices. Binarization offers an attractive solution by drastically reducing storage and computational costs. However, existing binarization methods neglect the varying importance of weights across different layers and modalities. This causes parameters irrelevant to downstream tasks to be unnecessarily retained, whereas modality-critical weights may not be adequately optimized, resulting in significant performance degradation. 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