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Multimodal geometric scores are surprisingly sensitive to the *direction* of modality degradation, not just its magnitude, revealing a critical geometric dependency previously overlooked.
Vision-language models can retain over 95% of their task accuracy under extreme 144脳 token compression without relying on destructive token pruning or expensive cross-attention resamplers.
