We propose a Hierarchically Nested Control Architecture for Artificial General Intelligence (HNCA-AGI) in which embodied cognition functions as an outer closed-loop system governing sensorimotor interaction with the environment, while reflection operates as an inner meta-cognitive loop that supervises, restructures, and recursively optimizes the outer loop’s cognitive and behavioral policies. This architecture integrates real-time adaptive control theory, recursive self-modeling, and multi-timescale learning to produce an AGI capable of both situated responsiveness and deliberative self-improvement.
The revolutionary insight is that human-level generality requires control-theoretic nesting of loops with distinct ontologies:
This dual-layer feedback system enables AGI to unify embodied intelligence and self-reflective intelligence into a single operational framework—something that has not yet been achieved in contemporary AI.