Memory Recall Mechanization as a Higher-Order Function (HOF)

One of the most significant challenges in developing Artificial General Intelligence (AGI) is managing long-term memory and maintaining context over extended, complex tasks. Traditional AI systems, particularly those based on deep learning, struggle with long-term dependencies due to their reliance on sequential processing and limited context windows. Research on psychophysical memory recall as a Higher-Order Function (HOF) addresses these challenges by proposing a novel approach to long-term memory management. By integrating higher-order programming principles with psychophysical theories, this framework offers a robust solution to overcoming the computational limitations that hinder AGI from engaging in long-term planning, task execution, and validation across extended projects.