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LLMs can achieve superior KV cache compression by intelligently allocating quantization budgets between layer-wise and token-wise strategies, outperforming uniform or single-granularity approaches.
Riemannian Geometry-Sensitive Quantization (RGSQ), which formulates quantization as a reconstruction problem under a unified Fisher-Riemannian metric, enabling standard unimodal PTQ methods to evaluate multimodal quantization error under their original assumptions.
