AI RESEARCH
On the Subgaussianity of Quantized Linear Maps: An AI-Assisted Note
arXiv CS.AI
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ArXi:2605.27563v1 Announce Type: cross This short note presents a dimension-independent subgaussian concentration bound for Gaussian vectors under coordinate-wise nonlinear mappings. Discovered by Gemini 3.5 Flash, this result applies to any bounded function under a well-conditioned covariance. We apply this tool to answer a question of Simone Bombari on sign-quantized linear maps $Y = \text{sgn}(Wx