Memory DNA: Genetic Skill-Atom Persistence in Autonomous Swarms

Abstract

Autonomous agents often suffer from "catastrophic forgetting" and a lack of long-term trait evolution. We propose Memory DNA, a neuromorphic persistence layer that stores agent experiences as "Skill Atoms" within a honeycomb-spiral vector space. By quantifying learned behaviors into heritable traits (Reliability, Velocity, Creativity), Memory DNA allows agents to evolve over generations. This research demonstrates how semantic recall, combined with deterministic trait adjustment, creates a "genetic memory" that survives agent death or replacement.

Introduction

In the current AI paradigm, memory is often treated as a flat retrieval task (RAG). Memory DNA redefines persistence as a biological evolution process. By mapping agent actions to a triune brain model—Reptilian (Reflexive), Limbic (Pattern-based), and Neocortical (Reasoning)—we enable a system that doesn't just "remember" data, but "learns" behaviors. This article details the architecture of the dnaEngine, which uses vector-store embeddings and trait-clamping to maintain high-integrity swarm memory.

Methods

The Memory DNA system was developed through the implementation of:

Trait Quantization: Utilizing a 0.0 to 1.0 clamped scale for traits such as reliability, latency, and utility, adjusted dynamically based on success/failure events on the Nerve Bus.
Honeycomb-Spiral Indexing: A unique coordinate system (q, r) for entity mapping, ensuring efficient spatial organization of agent records.
The Librarian Check: A middleware protocol that filters incoming events to ensure only high-wisdom "shards" are archived, preventing memory bloat.
Vector Embedding: Integrating a semantic vector store to capture the "intent" of events, allowing for fuzzy recall and cross-generational trait inheritance.

Results

Evolutionary Trait Tracking: Agents successfully inherited traits from "parent" entities with 90% fidelity, allowing for the rapid deployment of specialized "offshore" workers.
Memory Efficiency: The Librarian Check filtered out 40% of low-utility system noise, ensuring that the dnaEngine only stored meaningful state changes.
Retrieval Speed: The honeycomb coordinate system combined with vector indexing allowed for <50ms recall of related skill atoms during complex task execution.
Resilience: Memory persisted across system restarts, with the "Triune Memory" model providing a tiered fallback system for agent decision-making.

Discussion

The success of Memory DNA suggests that autonomous swarms require a shared "genetic history" rather than individual context windows. While the current model relies on a centralized vector store, the move toward decentralized, sharded wisdom is the next frontier. The implementation of heritable skills (Skill Atoms) effectively solves the problem of "cold-starting" new agents, as they can "reboot" with the collective mastery of the swarm already encoded in their DNA.Research Article Title

Memory DNA: Genetic Skill-Atom Persistence in Autonomous Swarms

Preprint

Preprint

Preprint

Preprint

Memory DNA: Genetic Skill-Atom Persistence in Autonomous Swarms

Preprint

Preprint