Self-evolving cognitive substrates through metabolic data processing and recursive self-representation with autonomous memory prioritization mechanisms.

Introduction: Conventional artificial intelligence (AI) systems are limited by static architectures that require periodic retraining and fail to adapt efficiently to continuously changing data environments. To address this limitation, this research introduces a novel biologically inspired computing paradigm that supports perpetual learning through continuous data assimilation and autonomous structural evolution. The proposed system aims to emulate biological cognition, enabling lifelong learning, self-repair, and adaptive evolution without human intervention. Methods: The system is built upon dynamic cognitive substrates that continuously absorb and map real-time information streams. These substrates eliminate the traditional distinction between training and inference phases, supporting uninterrupted learning. Quantum-inspired uncertainty management ensures computational robustness, while biomimetic self-healing protocols maintain structural integrity during adaptive changes. Additionally, micro-optimization via fractal propagation enhances mathematical specialization across hierarchical computational levels. Recursive learning mechanisms allow the architecture to refine its functionality based on its own outputs. Results: Experimental validation demonstrates that the proposed architecture sustains effective learning across diverse, heterogeneous data domains. The system autonomously restructures itself, maintaining stability while improving performance in dynamic environments. Specialized cognitive processing units, analogous to biological organs, perform distinct functions and collectively enhance adaptive intelligence. Notably, the system prioritizes and retains valuable information through evolution, reflecting biological memory consolidation patterns. Discussion: The findings reveal that continuous, self-modifying AI architectures can outperform traditional models in non-stationary conditions. By integrating quantum uncertainty control, biomimetic repair mechanisms, and fractal-based optimization, the system achieves resilient, autonomous learning over time. This approach has far-reaching implications for developing lifelong-learning machines capable of dynamic adaptation, self-maintenance, and evolution paving the way toward fully autonomous, continuously learning artificial organisms.