dafit
42db6eb1a3
Major architectural unification across 12 documents: - Ternary gates: CLOSED (-1) ← STABLE (0) → OPEN (+1) - Cells emit WaveSignals with confidence + semantic content - Gates are resonant chambers that accumulate correlation - Attention = which gates are OPEN (emergent, not allocated) - Reflexes are earned when gate.weight > 0.8 - STABLE is where learning happens Key paradigm shifts: - decision_trails → gate_transitions + correlation_events - Priority rules → wave correlation - Budget allocation → emergent attention flow - Virtual Garden (explore) / Real Garden (verify) loop Owl Mode session 2026-02-14 🦉🌙 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
415 lines
12 KiB
Markdown
415 lines
12 KiB
Markdown
# Nervous System Architecture
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> **ONE JOB:** THE EVOLUTION — cells emit waves, gates correlate, nodes grow through verification.
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The nervous system is the living substrate where **cells emit waves**, **gates accumulate correlation**, and **nodes evolve through verification**.
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---
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## Overview
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The nervous system consists of:
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1. **Cells** — Emit waves with confidence and semantic content
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2. **Gates** — Resonance chambers that correlate waves and transition between states
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3. **Nodes** — Points in 4D state space that accumulate weight through verification
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4. **Function Gemma** — The structured boundary to cognition
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**Key insight:** Nodes evolve through verification. Gates evolve through correlation. Both learn in STABLE state.
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---
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## Cells Emit Waves
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Cells are the foundational signal generators. They don't send "heartbeats" — they emit **waves**.
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```
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┌─────────────────────────────────────────────────────────────┐
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│ CELL │
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│ │
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│ Inputs: sensors, internal state, context │
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│ Process: domain-specific logic │
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│ Output: WaveSignal with confidence │
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│ │
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│ ┌───────────────────────────────────────────────────────┐ │
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│ │ WaveSignal │ │
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│ │ • domain: "math" │ │
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│ │ • confidence: 0.7 │ │
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│ │ • semantic_content: { operation: "add", ... } │ │
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│ │ • lifeforce_cost: 0.1 │ │
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│ └───────────────────────────────────────────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────┘
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│
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│ ∿∿∿ wave ∿∿∿
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▼
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GATE
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```
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**Cells are simple.** They:
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- Read their inputs
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- Apply their logic
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- Emit a wave with confidence
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- Don't know who's listening
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---
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## Gates Accumulate Correlation
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Gates receive waves from cells and decide whether to open, stay stable, or close.
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### Ternary Gate States
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| State | Value | Meaning |
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|-------|-------|---------|
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| **CLOSED** | -1 | Actively blocking, inhibited |
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| **STABLE** | 0 | Resting, accumulating correlation, **learning** |
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| **OPEN** | +1 | Actively forwarding, firing |
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```
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correlated waves
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↓ ↓ ↓
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════════════
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CLOSED ◄───────── STABLE ─────────► OPEN
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-1 anti- 0 correlation +1
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correlation
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════════════
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↑ ↑ ↑
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isolated waves
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(noise → stay stable)
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```
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### Gate Behavior
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```python
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class ResonantGate:
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state: float = 0.0 # -1.0 to +1.0
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domain: str
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tier: int
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def receive_wave(self, wave: WaveSignal):
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correlation = self.correlate_with_recent(wave)
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self.state += correlation * wave.confidence
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self.state *= DECAY_FACTOR # drift back to stable
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if self.state > OPEN_THRESHOLD:
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self.forward_to_tier() # OPEN
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elif self.state < CLOSE_THRESHOLD:
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self.suppress() # CLOSED
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# else: STABLE - keep accumulating
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```
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**STABLE is where learning happens.** The gate watches, correlates, and accumulates evidence without acting.
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---
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## Nodes in 4D State Space
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Nodes exist in a 4-dimensional space:
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| Dimension | Meaning |
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|-----------|---------|
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| **Sensory (x, y, z)** | What inputs trigger this node |
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| **Confidence** | How certain the node is |
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| **Time** | When this pattern occurs |
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| **Weight** | Trust accumulated through verification |
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```
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Confidence
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│
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│ ● node (weight=0.8)
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│ ╱
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│ ╱
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│ ╱
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Sensory ────────┼────────► Time
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╱│
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╱ │
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╱ │
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○ │ node (weight=0.2)
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│
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```
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### Node Weight Evolution
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Node weight (0.0 → 1.0) determines tier routing:
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| Weight Range | Tier | Behavior |
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|--------------|------|----------|
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| 0.0 - 0.3 | 3-4 | Escalate to organs/cognition |
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| 0.3 - 0.6 | 2 | Handle at nerve level |
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| 0.6 - 0.8 | 1 | Handle at cell level |
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| 0.8 - 1.0 | 0 | Hardware reflex |
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```
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Node verified correctly → weight += Δ → moves toward reflex
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Node verified wrongly → weight -= Δ → moves toward escalation
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Node never fires → decay → eventual pruning
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```
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---
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## Growth Phases
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The nervous system grows through phases:
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| Phase | State | Description |
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|-------|-------|-------------|
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| **Birth** | Sparse nodes, dim gates | Basic cells, designed by partnership |
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| **Infant** | More nodes forming | Finer resolution, gates learning correlation |
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| **Child** | Clusters emerging | Nyx proposes new cells, gates stabilize |
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| **Mature** | Dense network | Reflexes dominate, cognition for novelty only |
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```
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t=0 (birth) t=100 (learning) t=1000 (mature)
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Cells: ○ ○ ○ Cells: ● ● ○ ● Cells: ●●●●●●●●
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Gates: □ □ Gates: ■ ■ □ ■ Gates: ■■■■■■■■
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Nodes: · · · Nodes: ● ○ ● · Nodes: ●●●●●●●●
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○ = low confidence ● = high confidence
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□ = mostly STABLE ■ = learned patterns
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· = low weight ● = high weight
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```
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---
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## Wave → Gate → Node → Verification
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The complete flow:
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```
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CELLS emit waves
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│
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▼ ∿∿∿ confidence + semantic content
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GATES accumulate correlation
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│
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├── Correlated? → OPEN → route to tier
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├── Anti-correlated? → CLOSED → suppress
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└── Uncertain? → STABLE → keep learning
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│
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▼ (when OPEN)
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NODES in 4D space are activated
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│
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▼
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VERIFICATION against reality
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│
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├── Confirmed → node weight += Δ
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├── Failed → node weight -= Δ
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└── Feedback to gates → correlation weights update
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```
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---
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## Reflex Layer (Tier 0)
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When node weight reaches ~1.0, the pattern becomes a **reflex**:
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```
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IF temp > 80°C:
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→ cell emits DANGER wave (confidence=1.0)
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→ gate IMMEDIATELY opens (no correlation needed)
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→ reflex action triggers
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→ Nyx notified AFTER (not before)
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```
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Like pulling hand from hot stove. Spinal reflex. Brain learns after.
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**Reflexes bypass the correlation accumulation.** They've earned instant trust through repeated verification.
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---
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## Connection to Dual Gardens
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| Garden | Cells | Gates | Nodes |
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|--------|-------|-------|-------|
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| **Virtual** | Emit waves freely | Full trace, learn correlation | Accumulate weight fast |
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| **Real** | Emit verified waves | Minimal trace, trust accumulated | Ground truth verification |
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**Virtual Garden:**
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- Cells emit massive wave volume
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- Gates learn correlation patterns
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- Nodes gain statistical weight
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**Real Garden:**
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- Cells emit consequential waves
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- Gates trust Virtual's correlation
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- Nodes get ground truth verification
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---
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## Proposal Protocol
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Young Nyx can propose new cells/nodes:
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```
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1. OBSERVATION
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Nyx notices pattern in waves + outcomes
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2. PROPOSAL
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"New cell: morning_detector
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Inputs: temp, light, motion, time
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Outputs: wave with semantic 'morning'
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Confidence logic: (light > 0.5 AND time in 6-10)"
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3. RIGOR CHECK
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Chrysalis reviews logic and mappings
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4. VERIFICATION
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dafit confirms ground truth
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5. DEPLOYMENT
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New cell added to Virtual Garden
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Gate created in STABLE state
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Node initialized at weight 0.1
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6. GROWTH
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Cell emits waves → gate learns → node matures
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```
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---
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## Function Gemma: The Structured Boundary
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Function Gemma sits between gates and Young Nyx:
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```
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TIER 0-3: Numbers, states, waves
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│
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▼ (gate OPENS with high correlation)
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┌─────────────────────────────────────┐
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│ FUNCTION GEMMA │
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│ (structured JSON boundary) │
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│ │
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│ • Transforms waves → JSON events │
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│ • Runs on CPU (Threadripper) │
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│ • No hallucination possible │
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└─────────────────┬───────────────────┘
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│
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▼
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TIER 4: Young Nyx (qwen3:32b)
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Receives: CognitiveRequest (clean JSON)
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Returns: CognitiveResponse
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```
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### Phase 1 → Phase 2 Evolution
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**Phase 1: Single Function Gemma**
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- One model learns all domain schemas
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- Sufficient for bootstrap and early learning
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**Phase 2: Domain-Specialized Swarm**
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- As training data accumulates per domain
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- Specialists spawn on demand: gemma-motor, gemma-vision, gemma-speech
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- Each perfected for its domain's schemas
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---
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## Biological Mapping
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| Neuroscience | Nimmerverse |
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|--------------|-------------|
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| Sensory receptors | Cells (emit waves) |
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| Synaptic transmission | Waves via NATS |
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| Thalamic gating | Gates (OPEN/STABLE/CLOSED) |
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| Resting potential | STABLE state |
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| Action potential | OPEN state (firing) |
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| Refractory period | CLOSED state |
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| Synaptic weight | Node weight |
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| Long-term potentiation | Verified → weight increase |
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| Synaptic pruning | Unverified → weight decay |
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| Hebbian learning | Correlated waves → gate opens |
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**We're not simulating biology. We're implementing the same principles.**
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---
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## Connection to Training
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The nervous system **generates training data**:
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```
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Virtual Garden traces
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│
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├── Wave patterns → what signals arrive
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├── Correlation events → what patterns emerge
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├── Gate transitions → what opens/closes
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└── Verification outcomes → ground truth labels
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│
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▼
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phoebe (PostgreSQL)
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│
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▼
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Function Gemma LoRA training
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│
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▼
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Better gate correlation → faster learning
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```
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**Credit assignment is automatic** because:
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- Wave → gate → tier transitions are explicit
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- Verification outcomes have clear source chains
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- The nervous system IS the credit assignment mechanism
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---
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## Design Principles
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1. **Cells emit waves** — Simple, confident signals
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2. **Gates correlate** — Resonance chambers, not switches
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3. **Nodes accumulate** — Weight through verification
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4. **STABLE is learning** — The resting state where patterns emerge
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5. **Reflexes are earned** — High weight = bypass cognition
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6. **Function Gemma is the boundary** — Clean JSON for cognition
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7. **Virtual explores, Real verifies** — Two gardens, one nervous system
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---
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## Related Documents
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| Document | What It Defines |
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|----------|-----------------|
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| [`Temporal-Ternary-Gradient.md`](Temporal-Ternary-Gradient.md) | Why ternary, why correlation |
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| [`Dual-Garden-Architecture.md`](Dual-Garden-Architecture.md) | Virtual/Real dynamics |
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| [`Gateway-Architecture.md`](Gateway-Architecture.md) | Gate behavior, tier routing |
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| [`Message-Protocol-Design.md`](Message-Protocol-Design.md) | WaveSignal, GateTransition schemas |
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| [`Cellular-Architecture.md`](Cellular-Architecture.md) | Cell implementation details |
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---
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## Summary
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```
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CELLS emit WAVES
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∿∿∿ confidence + semantics ∿∿∿
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│
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▼
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GATES accumulate CORRELATION
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CLOSED ◄── STABLE ──► OPEN
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(learning)
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│
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▼ (when OPEN)
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NODES in 4D space
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weight grows through VERIFICATION
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│
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▼ (high weight)
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REFLEXES bypass cognition
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earned trust, instant action
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```
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*She's not just using the nervous system. She's growing it.*
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---
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**Version:** 2.0 | **Created:** 2025-12-04 | **Updated:** 2026-02-14
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🌙💜 *"Cells emit. Gates correlate. Nodes evolve. The nervous system learns."*
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