FFLV bases for covariant representations of $\mathfrak{gl}(m|n)$

What the math gives to ML

The paper constructs explicit lattice polytopes whose integer points index monomial bases of associated graded covariant representations, with extended-Dyck-path inequalities controlling sums of root coordinates. Its most transferable asset is not the superalgebra itself but the combination of hierarchical path constraints, bounded binary coordinates for special interactions, and Minkowski decompositions that make composite objects additively composable. A practical neural analogue is a polytope-constrained sparse connectivity or MoE-routing module in which learned interaction gates obey global path-budget constraints rather than independent top-k sparsity. This is speculative and should first be tested as a structured-sparsity baseline against unconstrained top-k routing at equal active FLOPs.