Far-from-equilibrium scaling of non-abelian Goldstone modes

What the math gives to ML

The paper develops a non-Abelian extension of KPZ dynamics in which a phase-like chronon couples to multiple Goldstone sectors. Its transferable mechanism is an RG-predictable relevance boundary: nonequilibrium couplings are relevant below effective dimension two, marginally relevant at dimension two, and irrelevant above dimension two. A neural-network analogue is a multi-channel sequence or state-space module with nonlinear gradient coupling, combined with a dimension-aware coupling schedule. The key falsifiable test is whether the learned coupling exhibits the predicted power-law growth, logarithmic growth, or decay under coarse-graining.