On a super-Virasoro group, a semigroup of annuli, and Gauss--Berezin integral operators

What the math gives to ML

The paper supplies an explicit centrally extended mode algebra whose commutator determines how structured transformations of Fourier features compose. This can be transferred into a neural module by replacing an unconstrained mixing matrix with exponentials of finitely many Virasoro generators, yielding a parameter-efficient mode mixer with analytically controlled composition rather than independently learned dense weights. The central extension provides an additional scalar channel that records opposite-mode interactions and may improve long-range frequency coupling. The most credible first test is a Fourier-domain residual block on periodic signals, comparing parameter count, stability, and extrapolation to unseen sequence lengths against a dense spectral mixer.