Abstract— Current trajectories in Artificial General Intelligence (AGI) development face significant structural bottlenecks, primarily driven by the unconstrained scaling of purely statistical predictors (Large Language Models). These systems lack an intrinsic understanding of natural constraints, feedback mechanisms, and boundary conditions, exposing them to epistemic fragility and catastrophic alignment failures. This paper presents a formal alternative architectural blueprint utilizing the Universal Formula and the paradigm of Self-Referential Ignorance (SRI). By embedding systemic laws—System Integrity, Balance, Feedback, Interconnected Nodes, and Hard Constraints—directly into the foundational operating logic of an AI agent, we provide a mathematical and structural path to achieve safe, stable, and self-correcting cognitive frameworks capable of navigating physical and economic systems without systemic disruption.
Angelito Enriquez Malicse (Tue,) studied this question.