Two-Layer Networks Provably Converge to Irreducible Group Representations

For the group composition task, projected gradient flow in the Fourier domain provably drives each neuron in a two-layer network to converge almost surely to a single irreducible representation under random initialization, with cross-layer Fourier coefficients achieving rotational rank-one alignment. This gives a rigorous representation-theoretic account of feature learning dynamics.