Open Problem: Separating Geometric and Algorithmic Compression via Cayley-Table Completion

ArXi:2605.29885v1 Announce Type: new Modern statistical learning theory and deep learning characterize generalization primarily in terms of continuous capacity control (e.g., norm-based regularization, margin maximization, low-rank bias). While highly successful in continuous domains, deep learning consistently fails to extrapolate exact algorithmic or discrete algebraic rules, reflecting a missing inductive bias toward algorithmic complexity minimization.