nimmerverse-sensory-network/archive/biomimetic-architecture.md

ADR-001: Biomimetic "Nimmerverse" Architecture

• Status: Accepted
• Date: 2025-12-05
• Context: Home Infrastructure / Autonomous Agent System
• Tags: biomimetic, event-driven, ai, local-llm

1. Context and Problem Statement

We are designing a local home infrastructure ("Nimmerverse") modeled after a biological organism. The goal is to create a system that is:

1. Reactive: Capable of sub-millisecond reflex responses (spinal layer) without waiting for heavy AI inference.
2. Deterministic: Preventing AI hallucination in critical control paths.
3. Evolvable: Allowing the system to "grow" new capabilities (nerves) through usage and verification.

The core challenge is balancing the high latency of Large Language Models (the "Brain") with the real-time requirements of home automation (the "Nervous System").

2. The Architecture: Hebbian-Reinforced Subsumption

We have adopted a Subsumption Architecture (popularized by Rodney Brooks) enhanced with a Hebbian Learning model ("neurons that fire together, wire together").

2.1 The 4D State Space (The Nervous System)

State machines replace standard "if/then" logic. Each state node exists in a 4-dimensional space:

• X/Y Dimensions: Sensory inputs (e.g., Temperature, Motion).
• Z Dimension (Confidence): A weight (0.0 - 1.0) representing reliability.
• Time Dimension: History of verification.

Lifecycle Logic:

• Birth: Node created at weight=0.1.
• Maturation: Successful triggers (verified by user) increase weight (+V).
• Pruning: Unused or falsified nodes decay and are removed.
• Reflex: Nodes with weight > 0.8 bypass the AI brain entirely for instant execution.

3. Feasibility Audit & Constraints

A. Metabolic Constraints (Hardware)

• Risk: Memory swapping kills agent reactivity.
• Requirement: The "Inference Orchestrator" (LLM) requires minimum 24GB VRAM to run a quantized 70B model, or distinct 12GB+ for a specialized 7B agent model. System RAM should be 64GB+ to handle the Vector DB and container orchestration.

B. Nerve Velocity (Transport)

• Pattern: Asynchronous Event Bus.
• Prohibition: HTTP/REST calls between "Organs" are forbidden due to blocking latency.
• Selected Tech: NATS or MQTT for the nervous system backbone.

C. Cognitive Load

• Bottleneck: The "Human Verification" step (dafit confirms) scales poorly.
• Mitigation: Implement "Sleep Cycles" where the system self-audits low-risk nodes against historical data during inactivity.

4. Implementation Strategy

Component	Biological Role	Technology Choice
State Engine	Nerves / Reflexes	XState (Actor-based state machines)
Vector Memory	4D Node Storage	Weaviate or Qdrant (Similarity search)
Event Bus	Nervous System	NATS (Low-latency messaging)
Orchestrator	Brain / Cognition	LocalAI or Ollama

5. Appendix: Interactive Simulation Logic

For the "Node Lifecycle" visualization widget:

• Visuals: A central node pulsing in a 2D grid.
• Variables: Confidence (Size/Glow), Age (Color).
• Logic:
  • IF verify_event THEN confidence += 0.1
  • IF falsify_event THEN confidence -= 0.2
  • IF confidence > 0.8 THEN status = 'REFLEX' (Gold Color)
  • IF confidence <= 0 THEN destroy_node()