RECURSIVE SELF-REFERENCE AS A STRUCTURAL PRINCIPLE FOR CONSCIOUS INTELLIGENCE: A DYNAMICAL FRAMEWORK LINKING PHILOSOPHY OF REALITY AND MACHINE LEARNING

Despite rapid progress in machine learning, a unified theoretical account explaining how intelligence develops from information processing into self-aware cognition remains unresolved [3], [4]. Current AI systems excel at prediction and optimization but lack persistent selfrepresentation and meta-cognitive awareness. This paper proposes recursive self-reference as a structural principle underlying conscious intelligence. We present a theoretical framework in which intelligent systems integrate external inputs with internally generated self-models through recursive feedback across time. Drawing on philosophy of mind, cognitive science, and computational learning theory, recursive processing is interpreted as enabling identity continuity, adaptive coherence, and meta-level evaluation [6], [7]. Consciousness is modeled as a dynamically stable regime of recursive informational organization rather than an unexplained emergent phenomenon. The framework suggests architectural extensions supporting explainability, continual learning, autonomous reasoning, and neuro-symbolic integration, providing a formal bridge between philosophical theories of reality and computational models of advanced artificial intelligence.