AI RESEARCH
Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization
arXiv CS.AI
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ArXi:2605.25203v1 Announce Type: cross We apply the influence-adaptive Walsh geometry of a companion theory paper (arXi:2605.01637) to extreme low-bit weight-only LLM quantization. The recipe is one math-invariant transformation: WHT-rotate each linear layer's weight matrix and rescale its columns by per-coordinate Walsh-basis activation energy before handing off to a reconstruction-error quantizer (Intel auto-round). This biases per-group integer rounding toward high-spectral-energy channels.