Structural General Intelligence

Large scale models have delivered extraordinary capabilities, yet scaling alone is unlikely to achieve the reliability, structural coherence, and adaptive intelligence required for real world general intelligence. These limitations stem not from insufficient training or missing data, but from the absence of stable identity, grounded distinctions, long horizon coherence, and continuous real world feedback. Structural General Intelligence (SGI) is introduced as a system level framework that unifies existing machine learning components, including world models, large and small language models, state space models, evaluators, routing mechanisms, inference time compute, and sleep time compute. SGI proposes that intelligence arises not from enlarging a single model but from the interaction of heterogeneous subsystems whose distinctions, constraints, and internal states converge toward a structural fixed point. The architecture centers on three interacting loops that support real time correction, intermediate reasoning, and slow structural consolidation. SGI reframes intelligence as coherence rather than magnitude, enabling systems to develop stable identity, self correction, specialization, and emergent reliability over long horizons. The framework explains why scaling hits structural and economic limits, how internal distinguishability underwrites emergent accuracy, and how SGI provides an integrative architecture that current ML systems can adopt without requiring new computational primitives.