Synergistic Coevolution of Cognitive Agency and Deep Learning Architectures for Self-Adaptive Intelligence

Abstract

The emergence of cognitive agency within artificial systems has transformed deep learning from a representational paradigm into a dynamic ecosystem of coevolving intelligence. As deep neural architectures grow increasingly complex, they exhibit behaviors akin to cognitive self-organization, enabling adaptive reasoning, contextual learning, and autonomous decision-making. This paper examines the synergistic relationship between cognitive agency and deep learning architectures, proposing that their coevolution forms the foundation of self-adaptive intelligence. It explores how neural architectures evolve through feedback-driven optimization and how agentic behavior arises from meta-learning, hierarchical abstraction, and continuous environmental interaction. The discussion traces this evolution from static data-driven models to self-regulating systems capable of reflective cognition and self-directed learning. By integrating cognitive theory with computational design, the paper argues that deep learning’s future lies not in greater scale but in its capacity to internalize agency — a process that redefines autonomy, adaptability, and the ontology of machine intelligence.