Abstract Current trajectories in Artificial General Intelligence (AGI) development face structural bottlenecks driven by the unconstrained scaling of purely statistical predictors. These systems lack an intrinsic accounting of their own epistemic limits, exposing them to hallucination and alignment failure under distributional shift. This paper advances a prior qualitative proposal (Malicse, 2026a) into a formal architecture by (i) defining a computable regularization term derived from the Self-Referential Ignorance scalar Ψ = M/K, grounded in the same Kolmogorov-complexity proxy validated empirically in Q-learning gridworld experiments (Malicse, 2026b) ; (ii) constructing a composite loss function L = LCE + λ·LSRI + μ·Lboundary that penalizes deviation from an empirically calibrated equilibrium band rather than merely gesturing at 'balance'; (iii) proving local asymptotic stability of the resulting training dynamics via a Lyapunov argument on the Ψ-trajectory; and (iv) deriving three falsifiable predictions that distinguish this architecture from RLHF-only baselines. The result converts the earlier five-law qualitative framework into a specific, testable modification of the training objective.
Angelito Enriquez Malicse (Tue,) studied this question.