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

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.