WUSH: Near-Optimal Adaptive Transforms for LLM Quantization 事件

WUSH: Near-Optimal Adaptive Transforms for LLM Quantization arXiv:2512.00956v3 Announce Type: cross Abstract: Quantizing LLM weights and activations is a standard approach for efficient deployment, but a few extreme outliers can stretch the dynamic range and amplify low-bit quantization errors. Prior transform-based mitigations (e.g., Hadamard rotations) are fixed and data-agnostic, and their optimality for quantization has remained unclear. We derive closed-form optimal linear blockwise transf