Thinking Machines: Foundations of General Intelligence and World Models

Despite significant advances in artificial intelligence, contemporary systems remain limited in their capacity to integrate fast adaptive behavior with structured abstract reasoning, capabilities that in humans emerge through continuous coordination across brain systems. While modern models achieve superhuman proficiency in narrow domains, they continue to falter at compositional reasoning, causal abstraction, and embodied understanding. In this first part of a two-part investigation, we analyze the foundational mechanisms underlying general intelligence through three interdependent dimensions: energy-efficient computation, embodied goal-directed control, and multi-timescale memory consolidation. Drawing on principles from neuroscience and systems theory, we argue that intelligence emerges not from monolithic optimization, but from the dynamic communication amongst processes operating across multiple temporal and representational hierarchies. We introduce a dual-loop computational framework, in which a fast inner loop supports real-time embodied adaptation, while a slower outer loop integrates memory, abstraction, and long-horizon planning. The architecture promotes energy-efficient through sparse, recurrent dynamics, maintains goal-directed behavior through predictive feedback and utility optimization, and constructs internal world models unifying perception, action, and inference. Together, these mechanisms outline a coherent foundation for scalable, adaptive, and energy-efficient general intelligence. The second part of this series extends these principles into a neuro-inspired framework for metacognitive control and self-reflective learning.