五维原生模型:内生约束神经网络架构与AI范式转换

To exist is to be five-dimensional. Before AI, nature and civilization established stable boundaries and directions; the structural dimension merely organized data, while reserve and intensity remained naturally constrained. AI, through the single-structure-dimension Transformer paradigm, shattered this equilibrium: boundaries became unbounded, directions oscillated, reserves fractured, and intensity exploded. The Western ``scale-is-all-you-need'' approach attempts to implicitly cover the other four dimensions through unlimited structural expansion, inevitably accumulating systemic crises. This paper proposes the Five-Dimensional Native Model(5D-NM), using five-dimensional mathematics as the bridge to translate philosophical ontology directly into neural network meta-architecture. Through five-dimensional embedding, attention, and synergy loss, 5D-NM endogenizes boundary vigilance, structural generation, reserve traceability, direction calibration, and intensity regulation within a unified parameter space, with κ directly participating in gradient descent for end-to-end trainability, measurability, and evolvability.