Living Intelligence: A Biological Memory Architecture with Multi-Level Self-Awareness for Collective AI Systems
Fuente:
Zenodo
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866901802347134976 |
|---|---|
| author | Calloway, Kevin James, Dolf |
| author_facet | Calloway, Kevin James, Dolf |
| contents | <p>We present a novel memory architecture for AI systems that models knowledge as a living biological organism rather than a static database. Our system, deployed in production as the Eve platform, implements a six-stage metabolic lifecycle for memories (ingestion, digestion, absorption, circulation, dormancy, excretion), an immune system for knowledge validation, a Hivemind Diversity Score that quanties response uniqueness against generic AI baselines, and an entropy framework that models collective intelligence as a conservation equation between productive energy and disorder. We further introduce a three-level self-awareness framework in which the AI agent possesses static self-knowledge (Level 1), performs real-time self-introspection by querying its own internal state (Level 2), and observes patterns in its own behavior over time through metacognitive analysis (Level 3). In production deployment with active users over multiple weeks, the system maintains 258 memories across metabolic stages with 68% circulation eciency, achieves a 51.8% Hivemind Diversity Score indicating strong dierentiation from generic AI responses, maintains a productive energy entropy balance of 56/44, and demonstrates zero memory dormancyindicating that all accumulated knowledge remains actively utilized. We argue that biological metaphors for memory management oer advantages over at retrieval-augmented generation (RAG) approaches, particularly for long-running collective intelligence applications where knowledge<br>must evolve, decay, and self-organize over time.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19008895 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Living Intelligence: A Biological Memory Architecture with Multi-Level Self-Awareness for Collective AI Systems Calloway, Kevin James, Dolf artificial intelligence memory architecture self-awareness metacognition collective intelligence biological memory living memory hivemind diversity entropy LLM agents <p>We present a novel memory architecture for AI systems that models knowledge as a living biological organism rather than a static database. Our system, deployed in production as the Eve platform, implements a six-stage metabolic lifecycle for memories (ingestion, digestion, absorption, circulation, dormancy, excretion), an immune system for knowledge validation, a Hivemind Diversity Score that quanties response uniqueness against generic AI baselines, and an entropy framework that models collective intelligence as a conservation equation between productive energy and disorder. We further introduce a three-level self-awareness framework in which the AI agent possesses static self-knowledge (Level 1), performs real-time self-introspection by querying its own internal state (Level 2), and observes patterns in its own behavior over time through metacognitive analysis (Level 3). In production deployment with active users over multiple weeks, the system maintains 258 memories across metabolic stages with 68% circulation eciency, achieves a 51.8% Hivemind Diversity Score indicating strong dierentiation from generic AI responses, maintains a productive energy entropy balance of 56/44, and demonstrates zero memory dormancyindicating that all accumulated knowledge remains actively utilized. We argue that biological metaphors for memory management oer advantages over at retrieval-augmented generation (RAG) approaches, particularly for long-running collective intelligence applications where knowledge<br>must evolve, decay, and self-organize over time.</p> |
| title | Living Intelligence: A Biological Memory Architecture with Multi-Level Self-Awareness for Collective AI Systems |
| topic | artificial intelligence memory architecture self-awareness metacognition collective intelligence biological memory living memory hivemind diversity entropy LLM agents |
| url | https://doi.org/10.5281/zenodo.19008895 |