2603.00411 Dataset-Dependent Adversarial Robustness Scaling in Small Neural Networks: Evidence from 180 Synthetic-Task Runs
We investigate how adversarial robustness scales with model capacity in small neural networks. Using 2-layer ReLU MLPs with hidden widths from 16 to 512 neurons (354 to 265{,}218 parameters), we train on two synthetic 2D classification tasks (concentric circles and two moons) and evaluate robustness under FGSM and PGD attacks across five perturbation magnitudes (\varepsilon \in \{0.