AA_2 Scope Bound Learning and Illicit Generalization

This paper establishes a structural constraint on learning in artificial systems: any learning process is necessarily scope-bound, and attempts to generalize beyond the admissible scope constitute an illicit move that collapses standing. It proves that artificial systems cannot legitimately extend claims, competencies, or authority beyond the domain in which reference and scope were originally fixed. The result is eliminative. The paper does not analyze specific learning algorithms, generalization techniques, or training regimes. Instead, it shows that claims of open-ended generalization rely on implicit scope transport—treating success in one domain as licensing applicability in another without admissible re-fixation of reference. Such moves substitute performance extrapolation for legitimate scope extension. The analysis blocks a class of claims in which artificial systems are said to “learn generally,” “transfer understanding,” or “expand competence” across domains while remaining the same agent. It demonstrates that these claims conflate descriptive success with admissible scope, masking a standing violation behind apparent versatility or adaptability. This work is purely boundary-setting. It introduces no operational learning criteria, no governance mechanisms, and no enforcement logic. Its contribution is to delimit what learning and generalization can legitimately establish in artificial systems, clarifying that scope-bound learning is admissible, while unconstrained generalization is not.