Gauge-Symmetry Knowledge Assembly and Big Knowledge Dynamics: Principles of Artificial Science I

Artificial intelligence is approaching a problem that cannot be reduced to the accumulation, retrieval, or representation of knowledge: heterogeneous bodies of knowledge must be assembled so that their local semantics can differ while their deeper structural relations remain operationally compatible. This paper proposes Artificial Science as a framework for this problem. Its central premise is that Integration Scienceidentifies portable mathematical structures across scientific domains, whereas Artificial Science converts such structural portability into executable mechanisms of knowledge transformation, composition, control, and stabilization. We develop the first unified model of this program throughgauge-symmetry knowledge assembly and formulate it as a theory of Big Knowledge Dynamics. The framework begins with a global-to-local geometric architecture in which knowledge contexts form a base space, structural symmetries act on internal representations, and connections, covariant derivatives, curvature, and holonomy govern transport across changing local frames. Within this architecture, four mechanism-specific sectors form a staged knowledge dynamics. provides the minimal model of covariant single-charge knowledge transport; provides a non-Abelian model of internally mediated multi-component assembly; supplies a doublet control architecture for externality-driven state transformation; and spontaneous symmetry breaking together with Higgs-like stabilization provides a model of locally stable yet adaptive rational organization. These sectors are not asserted to be ontologically identical to physical gauge theories. Their role is structural: each supplies a mathematically constrained interface that can be mapped into domain-specific knowledge systems and re-grounded in their own semantics. We combine these mechanisms into a staged assembly operator, A_BKD = S_stab ∘ X_SU(2) ∘ B_SU(3) ∘ T_U(1), representing transport, internal binding, externally induced transformation, and stabilization. The model separates representation change from content change, local curvature from global memory, internal mediation from external control, and stability from rigidity. Finally, we formulate a machine-facing layer in which formal objects are translated into computational objects, experimental manipulations, observable metrics, and falsifiable predictions. Gauge symmetry is thereby recast not as an analogy imported from physics but as a candidate structural technology for operational knowledge assembly. Artificial Science is proposed as the science of converting portable deep structures into executable and testable knowledge dynamics.