Neuro-ephemeral AI: adaptive neural framework for privacy-centered learning

This paper proposes Neuro-Ephemeral AI (NE-AI) framework that combines dynamic neural lifespan decay and memory osmosis to achieve adaptive learning and privacy-preserving knowledge management. Existing AI systems face challenges of perpetual knowledge retention, privacy risks due to static data storage, and inefficient resource usage in dynamic environments. NE-AI addresses these issues by gradually decaying unused information, diffusing knowledge across neurons, and implementing accelerated forgetting under privacy-compromising events. By doing so, NE-AI maintains system intelligence while ensuring compliance with data privacy standards, resulting in a more efficient and secure AI architecture. We evaluate NE-AI and demonstrate that it not only consistently outperforms existing approaches in accuracy retention but also operates with lower computational overhead, proving to be a practical and efficient solution for privacy-compliant neural network operations in dynamic environments.