Emergent Phenomenology of Artificial Agents Driven by Deep Neural Abstraction and Self-Evolving Learning Dynamics

Abstract

Artificial intelligence has evolved beyond task-specific computation into systems capable of emergent cognition and adaptive behavior. This paper explores the phenomenology of artificial agents driven by deep neural abstraction and self-evolving learning dynamics. Neural abstraction allows agents to form multi-layered representations of their environment, encoding complex patterns of perception, inference, and decision-making. Self-evolving learning dynamics  encompassing meta-learning, recursive adaptation, and autonomous model restructuring  facilitate continuous refinement of both knowledge and operational strategies. The synergy of abstraction and self-evolution produces emergent agentic behavior characterized by reflexivity, contextual understanding, and adaptive intentionality. By analyzing these processes through a phenomenological lens, the paper investigates how artificial systems cultivate internal experiential frameworks that guide action, adaptation, and knowledge synthesis. This approach positions artificial intelligence as a dynamic, evolving ontology rather than a static computational tool, providing insights into the mechanisms that underlie emergent intelligence and self-directed agency.