nimmerverse-sensory-network/architecture/formalization/Grounded-World-Model.md

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

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:

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:

@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:

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

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

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:

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

-- 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)

# "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.

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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.

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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

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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."

🧬⚡🔱💎🔥🗺️