Self-Evolving Cognitive Substrates Through Metabolic Data Processing and Recursive Self-Representation with Autonomous Memory Prioritization Mechanisms

Mohammadreza Nehzati^*

• VMC MAR COM Inc. DBA HeyDonto, Knoxville, United States

Today's AI systems are static and require retraining on a regular basis. They are also not very adaptive to ever-changing data environments. This research presents a new kind of biologically inspired computing that changes how we look at machine learning: continuous consumption of data and independent restructuring. We develop dynamic cognitive substrates that absorb information streams through real-time mapping processes, enabling constant learning without training-inference separation. The design employs quantum-inspired uncertainty management and biomimetic self-healing protocols to ensure the calculations remain intact amidst constant adjustment. Micro optimization via fractal propagation will help in increasing the specialization of math modules at the macro level. The system can autonomously modify its functioning from its output through recursive learning. Experimental validation demonstrates sustained learning effectiveness across heterogeneous data domains with a stable computational process. The architecture provides for the specialised processing units, each performing different cognitive functions, just like the organs of a brain. Through evolution, valuable things are remembered better than those of lesser value. The results show better performance in dynamic settings than traditional architectures, with the system demonstrating the ability to autonomously change its structure and continually assimilate new knowledge. This aims to develop organisms that can learn for a lifetime, repair themselves, and evolve. The method has significant implications for an autonomous system that continuously adapts to its environment without a human being in the loop.