Family Floer SYZ mirror algorithm for the Grassmannian $Gr(2,4)$

What the math gives to ML

The paper gives an explicit atlas of multiplicative cluster coordinates whose charts are glued by rational transformations involving additions of monomials, and whose non-archimedean valuations become piecewise-linear minimum operations. This suggests a structured neural layer that alternates between multiplicative coordinate changes and tropical, data-dependent branch selection rather than using unconstrained affine maps. The transferable asset is not the Grassmannian itself, but the combination of an invertible rational mutation, valuation-induced piecewise-linear behavior, and a chart-consistency constraint. A first test should insert one such stabilized cluster-mutation block into an MLP or transformer feed-forward sublayer and compare parameter efficiency, optimization stability, and accuracy against a similarly sized MLP.