Sefirot Continual Learning with Kabbalah-Tiered Memory and Hopfield-Amaru Associative Retrieval

Key Points

• The research aims to enhance continual learning in AI through novel memory architectures.
• Developed the Sefirot Continual Learner with graduated forgetting and EWC principles.
• Introduced Kabbalah-Tiered Memory encompassing Core, Working, and Episodic Memories with Ebbinghaus decay.
• Implemented Hopfield-Amaru Associative Memory using modern Hopfield networks with exponential capacity.
• Established a framework for improved memory retention with circular buffer architecture.
• Achieved significant performance improvements in AI memory management topics.