Cognitive Architecture for Emergent Artificial Intelligence through Entropy-Guided Self-Optimization

The Cognitive Simulation Engine introduces a modular cognitive architecture designed to investigate emergent intelligence through hierarchical information processing, entropy-guided optimization, and adaptive learning. The architecture is composed of three interconnected layers: an Information Layer responsible for perception and representation, a Learning Layer that develops internal models through experience, and a Meta-Optimization Layer that regulates global behavior using entropy-aware decision mechanisms.

Unlike conventional AI systems that rely on static optimization objectives, the Cognitive Simulation Engine continuously restructures its internal cognitive graph through feedback-driven adaptation. The proposed architecture integrates graph-based memory, dynamic topology optimization, cognitive node interactions, and entropy-based regulation to produce scalable and interpretable behavior.

Experimental evaluation demonstrates stable operation across thousands of cognitive nodes while maintaining computational efficiency. Ablation studies further validate the contribution of each architectural component toward adaptive reasoning and system performance.

This work presents the Cognitive Simulation Engine as a research framework for studying artificial cognition rather than a finished Artificial General Intelligence system. The architecture is intended as an extensible foundation for future investigations into autonomous learning, cognitive architectures, and self-organizing intelligent systems.