Single-Charge Knowledge-System Assembly and U(1) Symmetry: Principles of Artificial Science I (2)

The first paper of Principles of Artificial Science I proposed a global gauge architecture for Big Knowledge Assembly Dynamics. The present paper develops its first concrete mechanism: knowledge assembly under U(1) symmetry. The theoretical scaffold is quantum electrodynamics (QED), but the purpose is not to identify economic or cognitive systems with physical electromagnetic systems. Rather, QED provides a minimal mathematical structure for a single-charge dynamical system: a one-dimensional Abelian phase symmetry, a gauge potential, a covariant derivative, a field strength, and path-dependent phase transport. Previous work has independently constructed U(1)-type local dynamics in market dynamics and reasoning dynamics, within broader programs of economic dynamics and higher-cognition theory. The present paper does not reconstruct those local theories. It asks a new question: can previously separated U(1)-structured knowledge blocks be assembled through one reusable structural interface? We represent a local knowledge block by and introduce a common U(1) assembly template Each domain is related to this common template by a structural map Knowledge assembly therefore does not require QED, market dynamics, and reasoning dynamics to become identical. It requires designated structural relations to be preserved across domain-specific realizations. We formulate a preliminary criterion of U(1)-assemblability in terms of compatibility with covariant differentiation, curvature, and holonomy.The resulting framework is then applied to four contemporary AI problems: compositional generalization, continual learning and catastrophic forgetting, long-horizon trajectory drift, and cross-domain frame mismatch. In each case, the U(1) model suggests a common principle: knowledge should be transformed without confusing changes of representational frame with changes of structural content. The paper therefore interprets U(1) symmetry not merely as a shared mathematical form across disciplines, but as the first candidate technology for covariant AI knowledge assembly.