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March 16, 2026Open Access

Living Intelligence: A Biological Memory Architecture with Multi-Level Self-Awareness for Collective AI Systems

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Authors

KCKevin CallowayDJDolf James

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Overview

Novel memory architecture harnesses biological principles to enhance collective AI intelligence and self-awareness.

Key Points

  • The aim is to develop a memory architecture for AI systems that mimics biological processes and enhances collective intelligence.
  • Implemented a six-stage metabolic lifecycle for memories.
  • Developed a self-awareness framework with three levels: static knowledge, real-time introspection, and behavioral observation.
  • Utilized an immune system for validating knowledge and quantifying response uniqueness.
  • Deployed the architecture in a production environment with active users over several weeks.
  • Maintained 258 memories across metabolic stages with 68% circulation efficiency.
  • Achieved a 51.8% Hivemind Diversity Score, indicating strong differentiation from generic AI responses.
  • Demonstrated a productive energy balance of 56/44, indicating effective memory utilization.
  • Achieved a temporal reasoning accuracy of 94.6% in per-category analysis.

Cite This Study

Calloway et al. (2026) studied this question.

synapsesocial.com/papers/69b79e888166e15b153ac091https://doi.org/10.5281/zenodo.19023406
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