Language models perform well on grammatical agreement, but it is unclear whether this reflects rule-based generalization or memorization.We study this question for German definite singular articles, whose forms depend on gender and case.Using GRADIEND, a gradientbased interpretability method, we learn parameter update directions for gender-case specific article transitions.We find that updates learned for a specific gender-case article transition frequently affect unrelated gender-case settings, with substantial overlap among the most affected neurons across settings.These results argue against a strictly rule-based encoding of German definite articles, indicating that models at least partly rely on memorized associations rather than abstract grammatical rules.