Research questionHow can agnostic learning handle low-intrinsic-dimensional concepts under arbitrary distributions with Gaussian-robust benchmarks?Worst-case agnostic learning can be computationally hard even for simple concept classes, while existing tractable results often rely on highly structured instance distributions. Restricting the comparator to classifiers robust to small Gaussian perturbations changes the benchmark without requiring the data distribution itself to be structured.