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May 6, 20260 citationsOpen Access

Sefirot Continual Learning Using Kabbalah Memory and Hopfield Networks

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

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Authors

SLStephen Paul Jr. Lutar

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Overview

Sefirot continual learner improves working and episodic memory using hopfield networks, indicating advanced AI capabilities.

Key Points

  • The study aims to enhance continual learning in AI through innovative memory structures.
  • Developed Sefirot Continual Learner utilizing ten-tier EWC with graduated forgetting.
  • Implemented Kabbalah-Tiered Memory with core, working, and episodic elements alongside Ebbinghaus decay.
  • Combined modern Hopfield networks with exponential capacity for memory retention.
  • Introduced Ouroboros Conformal Memory as a circular buffer for data management.
  • Demonstrated improved retention in working memory compared to traditional models.
  • Enhanced retrieval capabilities via Hopfield networks in episodic memory tasks.

Cite This Study

Stephen Paul Jr. Lutar (2026) studied this question.

synapsesocial.com/papers/69faa2e204f884e66b5337afhttps://doi.org/10.5281/zenodo.20020844
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