Spectral nonassociative $\mathrm{L}^p$-spaces for $\mathrm{JBW}^*$-algebras

What the math gives to ML

The paper provides a principled spectral Lp norm for elements of a nonassociative Jordan algebra, including the exceptional Albert algebra that cannot be represented as an associative operator algebra. Its transferable asset is a trace-based spectral geometry that couples feature coordinates while retaining a mathematically valid norm and convexity structure. A practical neural-network test is to encode groups of features as small Hermitian matrices and use this norm for spectral feature scaling in residual or projection blocks. The idea is niche and introduces eigendecomposition overhead, but it is concrete and can be compared directly with RMSNorm and LayerNorm.