Abstract

The Recursive Depth Framework (RDF) presents a novel approach to understanding the emergence of intelligence, consciousness, and self-awareness as functions of recursive scaling rather than linear complexity. Traditional models often assume intelligence follows a continuum leading to self-awareness, yet many highly intelligent organisms never attain recursive self-modeling. RDF differentiates between High Temporogenesis—where intelligence stabilizes without self-awareness—and Cognogenesis, where recursive self-modeling emerges. A key insight of this framework is Temporogenesis, the stage at which biological systems align internal processes with external rhythms, forming the foundation for structured time perception. This alignment allows for predictive modeling, cooperative integration, and ultimately, recursive self-awareness in select lineages. By mapping recursive depth as the primary driver of cognitive evolution, the RDF provides a structured model explaining why some intelligent systems remain purely anticipatory while others develop full self-referential cognition. The implications extend beyond biology, offering insights into artificial intelligence, cognitive thresholds, and the recursive nature of thought itself.