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May 6, 2026Open Access

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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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Tiered Continual Learning (v7): Hopfield Associative Retrieval with Conformal Memory Bounds2026
  2. 2The Tidal Layer: Associative Memory for Persistent AI Agents2026
  3. 3Ecou Atemporal: A Biomimetic Cognitive Memory Architecture with Active Defense and Thermodynamic Retrieval Control (Warm Mirror Retrieval)2026
  4. 4Elastic Associative Memory: Memory That Evolves Without Retraining2026
  5. 5Oscillatory Associative Memory with Exponential Capacity2025 · 1 citations