Evolutionary Deep Learning Structures for Self-Supervising Efficiency, Latency Reduction, and Intelligent AI Task Propagation

Abstract

The rapid expansion of distributed artificial intelligence systems has intensified the demand for architectures capable of self-supervision, latency-aware decision-making, and intelligent task propagation across heterogeneous environments. Traditional deep learning pipelines often struggle with high computational overhead, rigid supervision requirements, and limited adaptivity when deployed in dynamic, real-time scenarios. This paper proposes an evolutionary deep learning framework designed to autonomously refine model efficiency, minimize execution latency, and propagate tasks intelligently without continuous external oversight. By integrating evolutionary strategies, self-supervision paradigms, and intelligent workload orchestration mechanisms, the proposed architecture achieves dynamic optimization across multiple layers of the learning and execution pipeline. The study further outlines how evolutionary processes enhance model robustness, enable reflective learning, and support adaptive resource allocation across diverse workloads. The findings highlight the transformative potential of integrating evolutionary intelligence within deep learning structures to create self-governing, latency-efficient, and task-aware AI ecosystems that scale seamlessly across emerging computational environments.