Exact Loop Controllers for ReLU Realization of Homogeneous Curve Refinements
Boldsaikhan Bolorkhuu, Tsogtgerel Gantumur

TL;DR
This paper develops exact ReLU neural network controllers for homogeneous curve refinement operators, enabling fixed-width, depth-linear realizations with geometric and residual dynamics control.
Contribution
It introduces an exact loop controller architecture for residual dynamics, extending scalar refinements to vector-valued, geometric constructions with fixed-width neural networks.
Findings
Exact ReLU realizations of refinement operators with fixed width and linear depth.
Construction of a geometric loop controller confining residual ambiguity.
Extension to affine forcing, polygonal generators, and complex Hilbert-type variants.
Abstract
We study homogeneous refinement operators \((V\gamma)(t)=\sum_{j\in\mathbb Z}A_j\gamma(Mt-j)\), acting on compactly supported continuous piecewise linear curves \(\gamma:\mathbb R\to\mathbb R^p\), where \(M\ge2\) and only finitely many matrices \(A_j\in\mathbb R^{p\times p}\) are nonzero. We prove that the iterates \(V^n\gamma\) admit exact ReLU realizations of fixed width and depth \(O(n)\). The main new ingredient is an exact loop controller for the residual dynamics. Instead of propagating scalar residual surrogates, the construction transports the residual orbit by a forward-exact state on a polygonal loop. Scalar factors and digit selectors are then recovered from this loop state by complementary CPwL readouts. The loop seam is not removed, but its remaining ambiguity is confined to the final readout/selector stage, where it is harmless because the scalar atom is supported away…
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