Constructive Intelligence: Deconstruction, Measurement, and Prediction of Deep Networks

Deep networks are a striking success yet remain a black box: we know the weights and activations exactly, but not what the network is actually constructing. Interpretability research has made mechanistic progress, yet lacks a unified, computable criterion for what the network is constructing. Applying the emergence criterion of the Wuliang Method (Paper I of this series) to AI, this paper proposes and argues the constructive-intelligence thesis: CNNs and Transformers are explicit instances of the constructive operator Omega. The thesis splits into a weak and a strong claim. The weak claim is honored here: the observable representations of a trained network can be read out as an emergence spectrum under the criterion protocol. The strong claim, that learning itself is the running of a constructive operator, is stated as a directional hypothesis with a formalization path. We give layer-by-layer formulas for Omega_CNN and Omega_TF, turning the black box into an auditable white-box construction chain.Scaling laws receive a topological deconstruction: power laws are projections of segments where E_i is identically zero, capability jumps are homological events on the stratified emergence spectrum, and two-peak loss curves acquire a topological embodiment. Self-recursion and hierarchical self-goal transitions are formalized; a minimal experiment shows self-goal transitions accelerate 48 times within the operator-compatibility domain. The statement that AI surpasses human intelligence receives a criterion-based formulation with review triggers. This is Paper II (AI applications) of the series; all criteria follow the computable, constructible, falsifiable discipline, unmeasured readings are left blank, and predictions are strictly separated from conclusions.