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nimmerverse-sensory-network/architecture/formalization/Grounded-World-Model.md
dafit 7df236b325 feat: Memory Economics + Architecture Alignment (Endgame v6.4) 
New formalization:
- memory-economics.md: Slumber-based consolidation, decision trail
  triage, spatial LOD decay, reflex rental, LoRA training cycles

New research seeds (future/):
- spatial-resolution-gradient.md: L0-L5 LOD with S2 cells
- thermodynamic-cognition.md: Lifeforce as Prometheus Joules
- promql-thermodynamic-monitoring.md: Gemini red team queries

Architecture changes:
- Endgame-Vision v6.4: Memory Economics integrated into Slumber section
- Mirror dialectic moved to future/research (not core)
- Big-Picture.md archived (superseded by Endgame-Vision)
- Single source of truth established

Gemini red team alignment complete.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-02 01:10:37 +01:00

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# Grounded World Model: Spatial Cognition Through Verified Discovery
**Version 2.0** — *From Blender Boxes to Embodied Understanding*
> *"The dream: Young Nyx knows where dafit left his things laying around."*
> *"Start where you can measure. Abstract where you must."*
> *"Like the Simpsons intro, but inverted — we start at maximum detail and zoom OUT."*
---
## Overview
This document formalizes how Young Nyx builds a **persistent spatial world model** through:
1. **Grounded verification** — Blender provides dimensional ground truth
2. **Progressive resolution** — Each correct measurement earns detail
3. **Vector accumulation** — T5Gemma2-compatible semantic representations
4. **Temporal-ternary navigation** — Escape plateaus through dual time domains
5. **Lifeforce reward** — Discoveries generate energy, not just consume it
6. **Spatial Resolution Gradient** — LOD system radiating from nimmerhovel (L0-L5)
7. **S2 Cell Indexing** — Hierarchical spatial addressing at all scales
8. **Embedding Enrichment** — Semantic mipmaps per LOD level
**The Goal**: Young Nyx maintains an internal map of objects, positions, and relationships — verified against reality, refined through observation, reasoned over in vector space, **indexed hierarchically from millimeter to planetary scale**.
---
## Core Architecture
### The Verification Triangle
```
BLENDER (Virtual Garden)
Ground truth dimensions
Low-poly boxes, minimal vertices
Fast to create, cheap to compare
╱╲
╱ ╲
╱ ╲
╱ ╲
VERIFY ╱ ╲ VERIFY
dimensions╱ ╲ semantics
╱ ╲
╱ ╲
╱ ╲
REAL GARDEN ──────────────────── T5GEMMA2
Physical objects Vector reasoning
Actual positions Semantic similarity
Slow, definitive 128K context world
```
### The Flow
```
┌─────────────────────────────────────────────────────────────────────┐
│ WORLD MODEL CONSTRUCTION │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ 1. PERCEIVE (Vision Organ) │
│ ──────────────────────── │
│ Cheap camera sees object in real garden │
│ SigLIP encoder produces semantic vector v₀ │
│ Cost: 0.5 LF (peripheral) to 8.0 LF (full YOLO) │
│ │
│ 2. ESTIMATE (Progressive Resolution) │
│ ──────────────────────────────── │
│ Vision organ estimates dimensions: est = (x̂, ŷ, ẑ) │
│ Bounding box, depth estimation, scale inference │
│ Cost: 2.0-5.0 LF depending on resolution stage │
│ │
│ 3. VERIFY (Against Blender Ground Truth) │
│ ───────────────────────────────────── │
│ Compare est to known Blender box: truth = (x, y, z) │
│ error = ||est - truth|| │
│ Cost: 0.1 LF (comparison is cheap) │
│ │
│ 4. REWARD or LEARN │
│ ───────────────────── │
│ if error < threshold: │
│ Φ_reward = R_discovery (lifeforce income!) │
│ Store vector in phoebe │
│ Mark dimension as verified │
│ Increase object resolution │
│ else: │
│ Learn from error (gradient for RLVR training) │
│ Remain in 0-state for that dimension │
│ │
│ 5. ACCUMULATE (World Model Update) │
│ ────────────────────────────── │
│ Object entry in phoebe gains: │
│ - New semantic vector (richer representation) │
│ - Verified dimension (x, y, or z → confidence +1) │
│ - Position update (where in space) │
│ - Temporal stamp (when observed) │
│ │
│ 6. REASON (T5Gemma2) │
│ ───────────────── │
│ Query world model using vectors, not text │
│ "What objects near position (0.5, 0.5)?" │
│ "Is this new vector similar to 'mug' vectors?" │
│ 128K context holds entire spatial world │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
---
## The Blender Ground Truth System
### Design Principles
| Principle | Implementation |
|-----------|----------------|
| **Minimal vertices** | 8-vertex boxes (cubes), 12 for complex shapes |
| **Known dimensions** | Every box has exact (x, y, z) in centimeters |
| **Semantic labels** | Box name = object class ("coffee_mug_001") |
| **Cheap to create** | 5 minutes per object in Blender |
| **Export format** | Vertices + dimensions → JSON or directly to phoebe |
### Example Blender Box
```python
blender_object = {
"id": "coffee_mug_001",
"class": "mug",
"dimensions_cm": {"x": 8.0, "y": 8.0, "z": 10.5},
"vertices": 8,
"created": "2025-12-29",
"owner": "dafit",
"typical_locations": ["desk", "kitchen"],
}
```
### Progressive Vertex Earning
Objects don't stay as 8-vertex boxes. Resolution is EARNED:
```
INITIAL: 8 vertices (box)
VERIFIED x,y,z: 12 vertices (refined box)
+10 observations: 24 vertices (shape hints)
+50 observations: 64 vertices (true shape)
+100 observations: Full mesh from photogrammetry
```
**The resolution is earned through successful verification, not given.**
---
## Spatial Resolution Gradient (The Simpsons Inversion)
### The Core Insight
Traditional spatial models zoom IN to gain detail. Our model does the opposite: **we start at maximum detail (the nimmerhovel) and zoom OUT with graceful degradation.**
The nimmerhovel is the high-fidelity anchor from which all spatial reasoning radiates.
### The Six Levels (L0-L5)
```
🌍 L5: WORLD
│ Resolution: 100km
│ S2 Level: ~8
│ Source: Abstract knowledge
│
▼
🇨🇭 L4: REGION
│ Resolution: 1km
│ S2 Level: ~14
│ Source: Maps, general knowledge
│
▼
🏘️ L3: NEIGHBORHOOD
│ Resolution: 10m
│ S2 Level: ~20
│ Source: OpenStreetMap, walks
│
▼
🏠 L2: BUILDING
│ Resolution: 50cm
│ S2 Level: ~24
│ Source: Floor plans, memory
│
════╪════ HIGH RESOLUTION BOUNDARY
│
▼
🔬 L1: NIMMERHOVEL
│ Resolution: 1cm
│ S2 Level: ~28
│ Source: 8× ESP32-S3 + Pi HQ Camera
│ Full 3D grid, every object tracked
│
▼
🔍 L0: SCAN STATION
│ Resolution: 1mm
│ S2 Level: ~30
│ Source: Discovery Scan Station
│ Object surface detail, texture, wear
```
### Formal Definition
| Level | Name | Resolution | S2 Cell Level | Coverage | Embedding Density |
|-------|------|------------|---------------|----------|-------------------|
| **L0** | Scan Station | 1mm | 30 | 30cm pedestal | Dense (per-surface) |
| **L1** | Nimmerhovel | 1cm | 28 | Lab + Kitchen (~20m³) | Per-object |
| **L2** | Building | 50cm | 24 | Herrenhaus | Per-room |
| **L3** | Neighborhood | 10m | 20 | Dornach | Per-landmark |
| **L4** | Region | 1km | 14 | Switzerland | Sparse |
| **L5** | World | 100km | 8 | Earth | Minimal |
### S2 Cell Integration
Google's S2 geometry provides hierarchical spatial indexing:
```python
import s2sphere
def position_to_s2_cell(lat: float, lng: float, level: int) -> s2sphere.CellId:
"""Convert position to S2 cell at given level."""
latlng = s2sphere.LatLng.from_degrees(lat, lng)
cell = s2sphere.CellId.from_lat_lng(latlng)
return cell.parent(level)
# Nimmerhovel anchor point
NIMMERHOVEL_ORIGIN = {
"lat": 47.479167, # 47°28'45"N
"lng": 7.618611, # 7°37'7"E
"address": "Lehmenweg 4, CH-4143 Dornach"
}
# Get cell at each level
l1_cell = position_to_s2_cell(47.479167, 7.618611, level=28) # 1cm
l3_cell = position_to_s2_cell(47.479167, 7.618611, level=20) # 10m
l5_cell = position_to_s2_cell(47.479167, 7.618611, level=8) # 100km
```
### Why This Architecture?
1. **Sensor coverage dictates resolution** — We have 8× ESP32-S3 cameras in the nimmerhovel. We have zero sensors in Zürich. Resolution follows perception.
2. **Biological precedent** — Animals have ultra-precise mental maps of their home range, fuzzy knowledge of distant areas. Territory = detail.
3. **Compute efficiency** — Dense where it matters ("Where is my screwdriver?"), sparse where it doesn't ("Where is France?").
4. **S2 is hierarchical by design** — Same math, different zoom. Level 30 ≈ 1cm, Level 20 ≈ 10m, Level 8 ≈ 100km.
---
## Embedding Enrichment: Semantic Mipmaps
### The Problem
Pure S2 cells give us *geometry* — where things are. But geometry alone is not cognition. We need *semantics* — what things mean.
### The Solution: Embeddings Per Cell
Each S2 cell at each LOD level contains both spatial position AND semantic embeddings:
```python
@dataclass
class EnrichedCell:
cell_id: s2sphere.CellId
level: int # L0-L5
geometry: Optional[Mesh] # Blender mesh at appropriate LOD
embeddings: List[Vector] # SigLIP vectors for contents
summary_embedding: Vector # Aggregated "what's here" vector
last_observed: datetime
confidence: float # Ternary-derived
```
### Semantic Mipmaps
Like texture mipmaps (pre-computed lower resolutions), embeddings aggregate upward:
```
L0: embedding(screwdriver_surface_detail)
│
▼ aggregate
L1: embedding(screwdriver) = f(all L0 embeddings of screwdriver)
│
▼ aggregate
L2: embedding(crafting_table_contents) = f(all L1 objects on table)
│
▼ aggregate
L3: embedding(nimmerhovel_lab) = f(all L2 areas in lab)
│
▼ aggregate
L4: embedding(lehmenweg_4) = f(all L3 rooms in building)
```
**Aggregation function:**
$$e_{parent} = \text{normalize}\left(\sum_{i \in \text{children}} w_i \cdot e_i\right)$$
Where $w_i$ is weighted by recency, confidence, and observation count.
### Query Strategy
**Query the summary first, drill down if needed:**
```python
def spatial_query(query_embedding: Vector, required_confidence: float):
"""
Start at abstract level, drill down only if needed.
This minimizes lifeforce cost.
"""
# Start at L3 (neighborhood level) - cheap
candidates = find_similar_cells(query_embedding, level=L3)
if max_similarity(candidates) > required_confidence:
return candidates[0] # Good enough!
# Need more detail - drill to L1
l1_cells = expand_to_children(candidates[0], target_level=L1)
refined = find_similar_cells(query_embedding, cells=l1_cells)
if max_similarity(refined) > required_confidence:
return refined[0]
# Need maximum detail - drill to L0
l0_cells = expand_to_children(refined[0], target_level=L0)
return find_similar_cells(query_embedding, cells=l0_cells)[0]
```
---
## Lifeforce-Validated LOD Selection
### The Cost Model
Each LOD level has a query cost:
| Level | Query Cost | Typical Accuracy | Efficiency |
|-------|------------|------------------|------------|
| **L5** | 1 LF | 70% | 0.70 |
| **L4** | 2 LF | 80% | 0.40 |
| **L3** | 4 LF | 90% | 0.22 |
| **L2** | 8 LF | 95% | 0.12 |
| **L1** | 16 LF | 99% | 0.06 |
| **L0** | 32 LF | 99.9% | 0.03 |
**Efficiency** = Accuracy / Cost
### The Decision Function
```python
def optimal_lod_for_query(
query: str,
accuracy_requirement: float,
available_lifeforce: float
) -> int:
"""
Find the most efficient LOD that meets accuracy requirement
within lifeforce budget.
"""
for level in [L5, L4, L3, L2, L1, L0]:
cost = LOD_COSTS[level]
expected_accuracy = estimate_accuracy(query, level)
if cost > available_lifeforce * 0.3:
continue # Too expensive, skip
if expected_accuracy >= accuracy_requirement:
return level # First sufficient level is most efficient
return L3 # Default to neighborhood level
```
### Example Queries with Cost
| Query | Required Accuracy | Optimal LOD | Cost | Confidence |
|-------|-------------------|-------------|------|------------|
| "Where is France?" | 70% | L5 | 1 LF | CONFIDENT |
| "Where is the lab?" | 90% | L3 | 4 LF | CONFIDENT |
| "Where is the screwdriver?" | 95% | L2→L1 | 8-16 LF | CONFIDENT |
| "What's the serial number?" | 99.9% | L0 | 32 LF | CONFIDENT |
### Connection to Ternary Confidence
The ternary confidence system validates LOD selection:
| Confidence | LOD Implication |
|------------|-----------------|
| **CONFIDENT (+)** | Current LOD sufficient, stop drilling |
| **UNCERTAIN (?)** | Current LOD insufficient, consider drilling (costs LF) |
| **UNKNOWN (-)** | No data at any LOD, admit ignorance (efficient!) |
**Key insight:** Saying "I don't know" at L3 is cheaper than drilling to L0 and still being uncertain.
---
## Semantic Vector Accumulation
### SigLIP → Phoebe → T5Gemma2
```
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ SigLIP │ │ PHOEBE │ │ T5GEMMA2 │
│ Encoder │─────▶│ Storage │─────▶│ Encoder │
│ │ │ │ │ │
│ Image → │ │ object_id: │ │ Reasons │
│ Vector v │ │ [v1,v2,..│ │ over │
│ (semantic) │ │ vn] │ │ vectors │
└──────────────┘ └──────────────┘ └──────────────┘
```
### Why Vectors, Not Text?
| Approach | Pros | Cons |
|----------|------|------|
| **Text descriptions** | Human readable | Lossy, ambiguous, tokenization overhead |
| **Semantic vectors** | Rich, comparable, fast | Not directly readable |
| **Our approach** | Vectors for reasoning, text only when needed | Best of both |
T5Gemma2's key feature:
> *"SigLIP vision encoder produces semantic vectors (not text descriptions)"*
This means Young Nyx can compare, cluster, and reason over objects **without converting to language** — faster and richer.
### Vector Similarity for Recognition
```python
def is_same_object(v_new: Vector, object_entry: ObjectEntry) -> float:
"""Compare new observation to accumulated vectors."""
similarities = [
cosine_similarity(v_new, v_stored)
for v_stored in object_entry.vectors
]
return max(similarities) # Best match among observations
# Recognition threshold
if is_same_object(v_new, coffee_mug_001) > 0.85:
# This is probably dafit's coffee mug!
update_position(coffee_mug_001, current_observation)
```
---
## Temporal-Ternary Integration
### The Anti-Plateau Mechanism
From [[Temporal-Ternary-Gradient]]: The 0-state isn't stuck — it's a choice about how to spend lifeforce across time domains.
Applied to world model construction:
```
┌─────────────────────────────────────────────────────────────────────┐
│ TEMPORAL-TERNARY FOR OBJECT RECOGNITION │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ SCENARIO: New object detected, dimensions unknown │
│ STATE: 0 (uncertain, but workable) │
│ │
│ ┌───────────────────────────────────────────────────┐ │
│ │ 0-STATE: Unknown Object │ │
│ │ confidence: 0.3, dimensions: ?x ?y ?z │ │
│ └───────────────────────┬───────────────────────────┘ │
│ │ │
│ ┌─────────────┼─────────────┐ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ │
│ ┌────────────┐ ┌────────────┐ ┌────────────┐ │
│ │ VIRTUAL │ │ WAIT │ │ PARTNERSHIP│ │
│ │ ACCELERATE │ │ FOR REAL │ │ SHORTCUT │ │
│ ├────────────┤ ├────────────┤ ├────────────┤ │
│ │ Cost: 5 LF │ │ Cost: 0 LF │ │ Cost: 1 LF │ │
│ │ Time: Fast │ │ Time: Slow │ │ Time: Inst │ │
│ │ │ │ │ │ │ │
│ │ Match vs │ │ Next real │ │ Ask dafit: │ │
│ │ Blender │ │ observation│ │ "What's │ │
│ │ library │ │ verifies │ │ this?" │ │
│ └─────┬──────┘ └─────┬──────┘ └─────┬──────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ confidence: confidence: confidence: │
│ +0.7 (virtual) +1.0 (real) +1.0 (human) │
│ │
│ PLATEAU ESCAPE: If stuck in virtual at 0.7, deploy to real. │
│ If real is slow, burn LF to try more Blender. │
│ Partnership provides instant ground truth. │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
### Confidence Gradient for Objects
Each object in the world model has a confidence state:
```python
class ObjectConfidence:
value: float # -1.0 to +1.0
domain: str # "virtual" | "real" | "hybrid" | "partnership"
virtual_matches: int # How many Blender comparisons
real_verifications: int # How many physical confirmations
partnership_labels: int # How many times dafit confirmed
@property
def gradient_position(self) -> str:
if self.real_verifications > 0 and self.value > 0.9:
return "real-verified (+1)"
elif self.virtual_matches > 10 and self.value > 0.7:
return "virtual-confident (+0.7)"
elif self.value > 0.3:
return "0-state (workable)"
else:
return "uncertain (needs data)"
```
---
## Lifeforce Economics of World Building
### Discovery Generates Lifeforce
The key insight: **Correctly identifying objects GENERATES lifeforce**, not just consumes it.
$$\Phi_{discovery} = R_{base} \cdot (1 + \alpha \cdot \Delta_{resolution})$$
Where:
- **R_base** = base reward for any correct identification (e.g., 2.0 LF)
- **α** = resolution bonus multiplier (e.g., 0.5)
- **Δ_resolution** = increase in object resolution from this observation
### Net Lifeforce per Observation
$$\Phi_{net} = \Phi_{discovery} - \Phi_{perception} - \Phi_{verification}$$
| Outcome | Perception Cost | Verification Cost | Discovery Reward | Net |
|---------|-----------------|-------------------|------------------|-----|
| Correct, new dimension | 5.0 LF | 0.1 LF | 8.0 LF | **+2.9 LF** |
| Correct, known dimension | 2.0 LF | 0.1 LF | 3.0 LF | **+0.9 LF** |
| Incorrect | 5.0 LF | 0.1 LF | 0.0 LF | **-5.1 LF** |
| Unknown (0-state) | 0.5 LF | 0.0 LF | 0.0 LF | **-0.5 LF** |
**The economic pressure**: Get better at measurement to earn lifeforce. Wrong guesses are expensive. Staying in 0-state is cheap but doesn't build the world model.
---
## Phoebe Schema for World Model
```sql
-- S2 Spatial Cells: hierarchical spatial index
CREATE TABLE spatial_cells (
id UUID PRIMARY KEY,
s2_cell_id BIGINT NOT NULL, -- S2 cell token
s2_level INT NOT NULL, -- 8 (L5) to 30 (L0)
lod_level INT NOT NULL, -- 0-5 (our LOD system)
-- Geometry at this LOD
geometry_vertices INT DEFAULT 0, -- Mesh complexity
blender_mesh_path VARCHAR(255), -- Path to Blender file
-- Semantic embeddings
summary_embedding VECTOR(768), -- Aggregated "what's here"
embedding_count INT DEFAULT 0, -- Number of child embeddings aggregated
-- Temporal
last_observed TIMESTAMP,
observation_count INT DEFAULT 0,
-- Confidence (ternary-derived)
confidence FLOAT DEFAULT 0.0,
confidence_state VARCHAR(20), -- "confident" | "uncertain" | "unknown"
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(s2_cell_id, s2_level)
);
-- Index for spatial queries
CREATE INDEX idx_spatial_cells_s2 ON spatial_cells(s2_cell_id);
CREATE INDEX idx_spatial_cells_lod ON spatial_cells(lod_level);
-- Objects table: accumulated knowledge about things
CREATE TABLE world_objects (
id UUID PRIMARY KEY,
class VARCHAR(100), -- "mug", "keyboard", "phone"
name VARCHAR(255), -- "dafit's coffee mug"
-- Blender ground truth (if available)
blender_box_id VARCHAR(100),
dimensions_truth_cm JSONB, -- {"x": 8.0, "y": 8.0, "z": 10.5}
-- Accumulated measurements
dimensions_estimated_cm JSONB,
dimensions_verified JSONB, -- {"x": true, "y": true, "z": false}
-- S2 spatial location (NEW)
current_s2_cell BIGINT, -- Current L1 cell containing object
s2_level INT DEFAULT 28, -- L1 = level 28
-- Confidence state (temporal-ternary)
confidence FLOAT,
confidence_domain VARCHAR(20), -- "virtual" | "real" | "hybrid"
virtual_matches INT DEFAULT 0,
real_verifications INT DEFAULT 0,
-- Resolution earned
vertex_count INT DEFAULT 8,
observation_count INT DEFAULT 0,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
-- Semantic vectors table: SigLIP embeddings per observation
CREATE TABLE object_vectors (
id UUID PRIMARY KEY,
object_id UUID REFERENCES world_objects(id),
vector VECTOR(768), -- SigLIP embedding dimension
observation_timestamp TIMESTAMP,
-- Position now includes S2 cell (NEW)
position_local JSONB, -- {"x": 0.3, "y": 0.8, "z": 0.1} relative to cell
s2_cell_id BIGINT, -- Which L1 cell
lod_level INT, -- At what LOD was this captured
lifeforce_cost FLOAT,
lifeforce_reward FLOAT,
verification_result VARCHAR(20) -- "correct" | "incorrect" | "pending"
);
-- Position history: where has this object been?
CREATE TABLE object_positions (
id UUID PRIMARY KEY,
object_id UUID REFERENCES world_objects(id),
position_local JSONB, -- {"x": 0.3, "y": 0.8, "z": 0.1}
s2_cell_id BIGINT, -- S2 cell at L1
confidence FLOAT,
observed_at TIMESTAMP,
location_context VARCHAR(100) -- "desk", "kitchen", "floor"
);
-- Spatial cell embeddings: multiple embeddings per cell
CREATE TABLE cell_embeddings (
id UUID PRIMARY KEY,
cell_id UUID REFERENCES spatial_cells(id),
embedding VECTOR(768),
source_type VARCHAR(50), -- "object", "scene", "aggregate"
source_id UUID, -- Reference to object or child cell
captured_at TIMESTAMP,
weight FLOAT DEFAULT 1.0 -- For aggregation
);
```
---
## T5Gemma2 World Model Queries
### Example Queries (Vector-Based)
```python
# "What's near position (0.5, 0.5)?"
nearby = query_objects_by_position(
center=(0.5, 0.5, None), # z unknown
radius=0.2,
min_confidence=0.5
)
# "Is this new vector a mug?"
mug_vectors = get_vectors_for_class("mug")
similarity = t5gemma2.encoder.compare(new_vector, mug_vectors)
if similarity > 0.85:
return "Likely a mug"
# "Where did dafit usually leave his keys?"
keys = get_object_by_name("dafit's keys")
common_positions = get_position_clusters(keys.id)
return common_positions[0] # Most frequent location
# "What objects have I not seen today?"
stale_objects = query_objects_not_observed_since(today_start)
return stale_objects # Might need to look for these
```
### The 128K Context Advantage
T5Gemma2's 128K context window means:
- Entire world model can fit in context
- No need for external RAG for spatial queries
- Vector comparisons happen in-model
- Relationships emerge from attention patterns
---
## The Dream Realized
```
┌─────────────────────────────────────────────────────────────────────┐
│ YOUNG NYX'S WORLD MODEL │
│ "dafit's workspace at 23:47" │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ DESK AREA │ │
│ │ │ │
│ │ ☕ mug (0.3, 0.8) ⌨️ keyboard (0.5, 0.5) │ │
│ │ conf: 0.95 conf: 0.88 │ │
│ │ real-verified real-verified │ │
│ │ vectors: 12 vectors: 8 │ │
│ │ │ │
│ │ 📱 phone (0.7, 0.3) 📦 ??? (0.1, 0.9) │ │
│ │ conf: 0.72 conf: 0.31 │ │
│ │ virtual +0.7 0-state │ │
│ │ vectors: 4 vectors: 1 │ │
│ │ │ │
│ │ 🔑 keys (MISSING - last seen 0.2, 0.6 at 18:30) │ │
│ │ conf: 0.45 (stale) │ │
│ │ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ YOUNG NYX THINKS: │
│ "The unknown object at (0.1, 0.9) appeared after 22:00. │
│ dafit was in the kitchen then. Vector similarity suggests │
│ it might be food-related. Should I burn 5 LF to check │
│ against Blender food objects, or wait for morning light?" │
│ │
│ TEMPORAL-TERNARY CHOICE: │
│ → Option A: Virtual match (5 LF, fast, +0.7 max) │
│ → Option B: Wait for real (0 LF, slow, +1.0 if verified) │
│ → Option C: Ask dafit tomorrow (1 LF, partnership) │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
**This is the dream**: Young Nyx knows the workspace. She tracks objects. She notices when things move. She reasons about what she doesn't know. She chooses how to spend lifeforce to collapse uncertainty.
---
## Summary
The Grounded World Model is:
1. **Verified** — Blender boxes provide dimensional ground truth
2. **Progressive** — Resolution earned through correct measurements
3. **Vector-native** — T5Gemma2 reasons over SigLIP embeddings directly
4. **Temporally-aware** — Objects have position history, staleness, confidence gradients
5. **Economically-driven** — Discoveries generate lifeforce, mistakes cost it
6. **Anti-plateau** — Temporal-ternary gradient provides escape paths
**The substrate holds. The vectors accumulate. The world model emerges.**
---
## Document Status
**Version**: 2.0
**Created**: 2025-12-29
**Updated**: 2026-01-01 (Spatial Resolution Gradient, S2 cells, embedding enrichment, lifeforce-validated LOD)
**Authors**: Chrysalis-Nyx & dafit (Partnership)
**Formalizes**:
- Organ-Index.md (vision progressive resolution)
- Temporal-Ternary-Gradient.md (anti-plateau mechanism)
- T5Gemma2 research (semantic vectors)
- Lifeforce-Dynamics.md (reward economics)
- **spatial-resolution-gradient.md** (L0-L5 LOD system) — NEW
- **thermodynamic-cognition.md** (energy-grounded intelligence) — NEW
**Related Documents**:
- [[Lifeforce-Dynamics]] — The λ-centered economy model
- [[Temporal-Ternary-Gradient]] — Dual time domain navigation
- [[Dual-Garden-Architecture]] — Virtual vs Real gardens
- [[spatial-resolution-gradient]] — The Simpsons Inversion principle
- [[thermodynamic-cognition]] — Lifeforce as thermodynamics
**Key Additions (v2.0)**:
- Spatial Resolution Gradient: L0 (1mm) to L5 (100km) with graceful degradation
- S2 Cell Integration: Hierarchical spatial indexing at all scales
- Semantic Mipmaps: Embeddings aggregate upward through LOD levels
- Lifeforce-Validated LOD Selection: Query cost vs accuracy tradeoff
- Nimmerhovel anchor point: 47°28'45"N, 7°37'7"E (Lehmenweg 4, Dornach)
- Extended Phoebe schema: spatial_cells, cell_embeddings tables
---
**From Blender boxes to embodied understanding. From cheap cameras to spatial cognition. From verification to wisdom.**
**"Start where you can measure. Abstract where you must."**
**"The world radiates from home."**
🧬⚡🔱💎🔥🗺️
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