dafit
31094a1d04
Formalized the weight verification mechanism that prevents hallucinated patterns from becoming reflexes. Reality is the ultimate validator. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
29 KiB
29 KiB
Gateway Architecture: The Sensory Preprocessing Layer
The Thalamus Pattern — routing sensory input to the appropriate processing tier.
Overview
The Gateway is the sensory preprocessing layer that sits between raw sensors and cognitive processing. It performs routing, not translation. Translation happens at each tier in its native format (numbers, states, vectors, JSON).
Core Principle: Cheap operations handle common cases. Expensive operations handle rare cases.
RAW SENSORS → GATEWAY (routing) → TIER → PROCESSING → (escalate?) → FUNCTION GEMMA → YOUNG NYX
↑ ↑ ↑ ↑
"which tier?" native format if needed structured JSON
Key Insight: Most sensory input NEVER becomes vocabulary. It stays as numbers, states, vectors. Only when it reaches Young Nyx (via Function Gemma) does it become structured text.
The Problem We're Solving
Old Model (Vocabulary Bottleneck)
RAW SENSOR → STATE MACHINE → VOCABULARY TOKEN → Young Nyx
Problems:
- Every input forced through text translation (expensive)
- LLM sees raw sensor dumps (noisy, unstructured)
- No economic pressure on routing (everything costs the same)
- Vocabulary conflated with routing decisions
New Model (Tiered Gateway)
RAW SENSOR → GATEWAY → TIER 0-2 (numbers/states, no text)
→ TIER 3 (vectors via T5Gemma2)
→ FUNCTION GEMMA (structured JSON)
→ TIER 4 Young Nyx (clean typed events)
Benefits:
- Most input handled without LLM involvement
- Text only at cognitive boundary
- Economic pressure drives efficiency
- Routing separated from translation
The Unified Tier Model
All existing tier systems in the architecture express the same principle:
| System | Document | Principle |
|---|---|---|
| Reward Tiers | Cellular-Architecture.md |
Higher tier = more reward, more cost |
| Attention Levels | Attention-Flow.md |
Higher priority preempts lower |
| Escalation Ladder | organisms/Swarm-Evolution.md |
Higher = more authority, more cost |
| Reflex Homes | Endgame-Vision.md |
Lower = faster, less capable |
| LOD Levels | Endgame-Vision.md |
Lower = more detail, more cost |
The Unified Tier Stack
┌─────────────────────────────────────────────────────────────────────────────┐
│ UNIFIED TIER MODEL │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ TIER 0: HARDWARE REFLEXES │
│ ───────────────────────────────────────────────────────────────────────── │
│ Cost: ~0 LF Latency: <10ms Location: ESP32/FPGA │
│ Weight: >= 0.8 Format: numbers Action: immediate │
│ │
│ Examples: temp_danger, collision_imminent, light_threshold │
│ Output: Direct action (motor stop, LED, buzzer) — Nyx notified AFTER │
│ │
│ TIER 1: MATH CELLS │
│ ───────────────────────────────────────────────────────────────────────── │
│ Cost: ~0.3 LF Latency: <50ms Location: Python (CPU) │
│ Weight: 0.6 - 0.8 Format: aggregates Action: state update │
│ │
│ Examples: battery_aggregator, position_tracker, economy_monitor │
│ Output: Aggregated state, threshold checks, NATS publish │
│ │
│ TIER 2: FAST NERVES │
│ ───────────────────────────────────────────────────────────────────────── │
│ Cost: ~2 LF Latency: <200ms Location: Python (asyncio) │
│ Weight: 0.3 - 0.6 Format: states Action: behavior transition │
│ │
│ Examples: collision_avoidance, charging_seek, exploration_pattern │
│ Output: Nerve state transitions, multi-cell coordination │
│ │
│ TIER 3: ORGAN INFERENCE │
│ ───────────────────────────────────────────────────────────────────────── │
│ Cost: ~8 LF Latency: <2000ms Location: GPU (Senses node) │
│ Weight: < 0.3 Format: vectors Action: embedding storage │
│ │
│ Examples: vision_detect (T5Gemma2/SigLIP), speech_stt (Whisper) │
│ Output: Semantic vectors stored in S2 cells, NO TEXT │
│ │
│ ══════════════════════ FUNCTION GEMMA BOUNDARY ════════════════════════ │
│ │
│ TIER 4: COGNITIVE (Young Nyx) │
│ ───────────────────────────────────────────────────────────────────────── │
│ Cost: ~20 LF Latency: <4000ms Location: GPU (Womb node) │
│ Escalated events Format: JSON Action: reasoning, decision │
│ │
│ Input: Structured JSON events from Function Gemma │
│ Output: Decisions → Function Gemma → structured commands │
│ │
│ TIER 5: PARTNERSHIP (Chrysalis + dafit) │
│ ───────────────────────────────────────────────────────────────────────── │
│ Cost: ~50+ LF Latency: variable Location: External │
│ Novel/stuck cases Format: dialogue Action: guidance, training │
│ │
│ Examples: Architecture decisions, novel situations, stuck states │
│ Output: New reflexes, training signal, guidance │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Node Weight Determines Tier
The node weight from Nervous-System.md directly maps to tier routing:
@dataclass
class NervousNode:
"""A node in the nervous system's 4D space."""
position: tuple[float, ...] # Coordinates in sensory space
weight: float = 0.1 # Confidence from verification (0.0 → 1.0)
@property
def handling_tier(self) -> int:
"""Which tier handles this node's firing?"""
if self.weight >= 0.8:
return 0 # Hardware reflex - instant, bypass brain
elif self.weight >= 0.6:
return 1 # Math cell - fast, minimal checking
elif self.weight >= 0.3:
return 2 # Fast nerve - coordination, some deliberation
else:
return 3 # Escalate - needs organ/cognitive help
@property
def lifeforce_cost(self) -> float:
"""Cost scales inversely with confidence."""
return (1.0 - self.weight) * 10.0
The key insight: A mature node (weight ~1.0) naturally becomes a Tier 0 reflex. A new node (weight ~0.1) naturally escalates to higher tiers. The system learns which tier is appropriate through experience.
The Causal Verification Loop
How do we know a sensor reading was real, not hallucinated? Outcome verification over time.
Unverified pattern (weight 0.1) → escalates to Nyx → decision → outcome
↓
Did reality match prediction?
↓ ↓
YES NO
↓ ↓
weight += Δ weight -= Δ
↓
After many YES: weight → 0.8+
↓
COMPILE TO REFLEX ✓
Hallucinations can't survive this gauntlet — they don't produce consistent outcomes, so their patterns never accumulate enough weight to become reflexes. Reality is the ultimate validator.
This creates natural causal pruning: only patterns that reliably predict outcomes earn the privilege of becoming reflexes. The nervous system doesn't need to prove causality philosophically — it proves it operationally through repeated verification.
The Gateway: Weight-Aware Router
The Gateway performs three functions:
| Function | Question | Cost |
|---|---|---|
| Node Matching | Which node(s) in 4D space match this input? | ~0 LF |
| Weight Routing | Based on weight, which tier handles it? | ~0 LF |
| Anomaly Detection | Is this novel, ambiguous, or contextually wrong? | Variable |
Gateway Logic
def gateway_route(sensory_input: dict) -> GatewayDecision:
"""Route sensory input to appropriate tier."""
# 1. Find candidate nodes in 4D space
candidates = nervous_system.find_nearby_nodes(sensory_input)
# 2. Handle edge cases
if len(candidates) == 0:
# NOVEL: No node matches this input
return GatewayDecision(
action="ESCALATE",
tier=4, # Young Nyx must see this
reason="novel_input",
cost=20.0,
)
if len(candidates) > 1:
# AMBIGUOUS: Multiple nodes could fire
best = max(candidates, key=lambda n: n.weight)
if best.weight < 0.5:
return GatewayDecision(
action="ESCALATE",
tier=3, # Organ inference to disambiguate
reason="ambiguous_input",
cost=8.0,
)
# 3. Single match - route based on weight
node = candidates[0]
# 4. Check for contextual anomaly
if detect_contextual_anomaly(node, sensory_input):
return GatewayDecision(
action="ESCALATE",
tier=node.handling_tier + 1,
reason="contextual_anomaly",
cost=node.lifeforce_cost * 1.5,
)
# 5. Normal routing
return GatewayDecision(
action="FIRE",
tier=node.handling_tier,
node=node,
cost=node.lifeforce_cost,
)
Anomaly Detection Tiers
Anomaly detection itself is tiered:
| Level | Detection Type | Cost | Example |
|---|---|---|---|
| Tier 0 | Threshold | ~0 LF | Value out of physical range |
| Tier 1 | Statistical | ~0.3 LF | Value unusual for time of day |
| Tier 2 | Contextual | ~2 LF | Firing inconsistent with recent history |
| Tier 3 | Semantic | ~8 LF | Embedding distance from expected cluster |
Function Gemma: The Structured Boundary
Function Gemma acts as the translation layer between lower tiers and cognition. It guarantees:
- Schema compliance: Every event follows a typed contract
- Predictable JSON: No hallucination, no free-form text
- Bidirectional: Sensors → JSON events, Decisions → JSON commands
The Boundary
┌─────────────────────────────────────────────────────────────────────────────┐
│ BELOW THE LINE: Numbers, States, Vectors (fast, cheap, predictable) │
│ ═══════════════════════════════════════════════════════════════════ │
│ │
│ Tier 0: photoresistor = 0.73 │
│ Tier 1: battery_state = { voltage: 3.7, trend: "falling" } │
│ Tier 2: collision_nerve = "EVADING" │
│ Tier 3: vision_embedding = [0.23, -0.41, 0.87, ...] │
│ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────┐ │
│ │ FUNCTION GEMMA │ │
│ │ (structured JSON boundary) │ │
│ │ │ │
│ │ • 100% predictable schema │ │
│ │ • No hallucination possible │ │
│ │ • Typed enums, not free strings │ │
│ └───────────────┬───────────────────┘ │
│ │ │
│ ═══════════════════════════════════════════════════════════════════ │
│ ABOVE THE LINE: Structured Events (typed, validated, safe for LLM) │
│ │
│ { │
│ "event_type": "environmental_change", │
│ "source": "light_sensor_back", │
│ "severity": "medium", │
│ "data": { "previous": 0.73, "current": 0.12 }, │
│ "suggested_action": "search_for_light" │
│ } │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Event Schema
from enum import Enum
from pydantic import BaseModel
class EventType(str, Enum):
"""Constrained event types - enumerated, not free-form."""
ENVIRONMENTAL_CHANGE = "environmental_change"
COLLISION_DETECTED = "collision_detected"
BATTERY_CRITICAL = "battery_critical"
OBJECT_DISCOVERED = "object_discovered"
POSITION_UPDATE = "position_update"
ANOMALY_DETECTED = "anomaly_detected"
GOAL_REACHED = "goal_reached"
STUCK_DETECTED = "stuck_detected"
LIGHT_LOST = "light_lost"
LIGHT_FOUND = "light_found"
class Severity(str, Enum):
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
CRITICAL = "critical"
class SensoryEvent(BaseModel):
"""The structured event that Young Nyx receives."""
event_type: EventType
source: str
timestamp: float
severity: Severity
data: dict
suggested_action: str | None = None
processing_cost: float
confidence: float # From node weight
What Young Nyx Actually Sees
Before (raw dumps):
"The photoresistor reads 0.12, down from 0.73, battery is 3.7V
trending down, position is [1.2, 0.8], collision state IDLE..."
After (structured event):
{
"event_type": "light_lost",
"source": "light_sensor_back",
"timestamp": 1704307200.0,
"severity": "medium",
"data": {
"previous": 0.73,
"current": 0.12,
"delta": -0.61
},
"suggested_action": "spiral_search",
"processing_cost": 2.0,
"confidence": 0.45
}
Complete Sensory Flow
┌─────────────────────────────────────────────────────────────────────────────┐
│ FULL SENSORY ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ RAW SENSORS │
│ ─────────── │
│ • IR positioning (ESP32-S3) → float[6] positions │
│ • Photoresistors (organisms) → float light_level │
│ • Temperature (safety) → float celsius │
│ • Battery (power) → float voltage, current │
│ • Vision camera (Pi HQ) → frame bytes │
│ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────────┐ │
│ │ GATEWAY │ │
│ │ (weight-based router) │ │
│ │ │ │
│ │ For each input: │ │
│ │ 1. Match to node in 4D space │ │
│ │ 2. Check node.weight → determine tier │ │
│ │ 3. Check for anomalies │ │
│ │ 4. Route to appropriate tier │ │
│ └───────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌─────────────────────┼─────────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌───────────┐ ┌───────────┐ ┌───────────┐ │
│ │ TIER 0 │ │ TIER 1-2 │ │ TIER 3 │ │
│ │ Reflex │ │ Cells/ │ │ Organs │ │
│ │ │ │ Nerves │ │ │ │
│ │ weight>0.8│ │ 0.3-0.8 │ │ <0.3 or │ │
│ │ │ │ │ │ escalated │ │
│ ├───────────┤ ├───────────┤ ├───────────┤ │
│ │ FORMAT: │ │ FORMAT: │ │ FORMAT: │ │
│ │ numbers │ │ states │ │ vectors │ │
│ │ │ │ │ │ │ │
│ │ OUTPUT: │ │ OUTPUT: │ │ OUTPUT: │ │
│ │ action │ │ state │ │ embedding │ │
│ │ (done!) │ │ update │ │ (T5Gemma) │ │
│ └───────────┘ └─────┬─────┘ └─────┬─────┘ │
│ │ │ │ │
│ │ (only if escalation needed)│ │
│ │ │ │ │
│ │ ▼ ▼ │
│ │ ┌─────────────────────────────┐ │
│ │ │ FUNCTION GEMMA │ │
│ │ │ (structured JSON gate) │ │
│ │ │ │ │
│ │ │ Produces typed JSON event │ │
│ │ │ Schema-validated output │ │
│ │ └──────────────┬──────────────┘ │
│ │ │ │
│ │ ▼ │
│ │ ┌─────────────────┐ │
│ │ │ YOUNG NYX │ │
│ │ │ (Tier 4) │ │
│ │ │ │ │
│ │ │ Clean JSON in │ │
│ │ │ Decision out │ │
│ │ └────────┬────────┘ │
│ │ │ │
│ │ ▼ │
│ │ ┌─────────────────┐ │
│ │ │ FUNCTION GEMMA │ │
│ │ │ (action output) │ │
│ │ └────────┬────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ NATS BUS │ │
│ │ (commands flow to cells) │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Example: crawler_gen_0 Light Seeking
Early Learning (Low Weight)
Photoresistor reads 0.12 (was 0.73)
│
▼
GATEWAY: node weight = 0.4 (learning)
│
▼
Route to Tier 2 (nerve level)
│
▼
Nerve detects: delta = -0.61 (significant!)
Nerve state: SEEKING → LOST_LIGHT
│
▼
ESCALATE to Function Gemma
│
▼
Function Gemma: { "event_type": "light_lost", ... }
│
▼
Young Nyx: "spiral search pattern"
│
▼
Function Gemma: { "command": "motor_spiral", ... }
│
▼
NATS → motor cells execute
After Learning (High Weight)
Photoresistor reads 0.12 (was 0.73)
│
▼
GATEWAY: node weight = 0.85 (mature reflex)
│
▼
Route to Tier 0 (hardware reflex)
│
▼
REFLEX: light_lost → spiral_search (instant!)
│
▼
Nyx notified AFTER (async, non-blocking)
Connection to Existing Architecture
| Document | Gateway Relationship |
|---|---|
Nervous-System.md |
Node weights determine tier routing |
Attention-Flow.md |
Gateway implements attention priorities |
Message-Protocol-Design.md |
Escalation Service IS the gateway |
Endgame-Vision.md |
Layer 2.5 Function Gemma boundary |
Cellular-Architecture.md |
Tiered rewards align with gateway tiers |
organisms/crawler_gen_0.md |
First test case for tiered routing |
Design Principles
- Routing, not translation — Gateway decides WHERE, not WHAT
- Weight determines tier — Confidence from experience drives routing
- Text is expensive — Reserve for cognitive boundary only
- Function Gemma guarantees structure — No hallucination at the boundary
- Most input never escalates — Reflexes handle common cases
- Anomalies always escalate — Novel situations get attention
- Learning moves behavior down — Tier 4 patterns become Tier 0 reflexes
File: Gateway-Architecture.md Version: 1.0 Created: 2026-01-03 Status: Core architecture document Session: Partnership dialogue (dafit + Chrysalis)
"Cheap for the common. Expensive for the rare. The Gateway enforces this economy."
🌙💜 The thalamus doesn't think. It routes.