nimmerverse-sensory-network/architecture/future/spatial-resolution-gradient.md

Spatial Resolution Gradient: LOD for Cognitive Space

Origin: New Year's Day 2026, post-nimmerhovel measurement session Authors: dafit + Chrysalis-Nyx Status: Architectural concept / Foundation for artifact data model Related: concept-token-pairs.md (Spatial Grounding section), artifact data model task

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The Insight

"Like the Simpsons intro, but inverted."

The Simpsons intro zooms from space → Earth → Springfield → house → couch → Homer's head, gaining detail as it approaches.

Our spatial model does the opposite: we start at maximum detail (nimmerhovel) and zoom OUT with graceful degradation.

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The Resolution Gradient

                        🌍 EARTH
                        │  S2 cell level ~10
                        │  "Somewhere in Europe"
                        │
                    ════╪════ ABSTRACTION BOUNDARY
                        │
                        ▼
                   🇨🇭 SWITZERLAND
                        │  S2 cell level ~15
                        │  "Northwestern region"
                        │
                        ▼
                   🏘️ DORNACH
                        │  S2 cell level ~20
                        │  Key landmarks: Goetheanum, station
                        │
                        ▼
                   🏠 LEHMENWEG 4
                        │  Building footprint
                        │  "5th floor attic"
                        │
                    ════╪════ HIGH RESOLUTION BOUNDARY
                        │
                        ▼
                   🔬 NIMMERHOVEL
                        │  1cm grid resolution
                        │  Every object tracked
                        │  Full camera coverage
                        │  GROUND TRUTH ZONE
                        │
                        ▼
                   🔍 DISCOVERY SCAN STATION
                        │  Sub-millimeter
                        │  Object embeddings
                        │  Maximum detail

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Resolution Layers

Layer	Name	Resolution	Source	Coverage
L0	Scan Station	1mm	Discovery Scan Station, SigLIP	30cm × 30cm pedestal
L1	Nimmerhovel	1cm	8× ESP32-S3 + Pi HQ Camera	Lab + Kitchen (~20m³)
L2	Building	50cm	Floor plans, memory	Herrenhaus
L3	Neighborhood	10m	OpenStreetMap, walks	Dornach
L4	Region	1km	Maps, general knowledge	Switzerland
L5	World	100km	Abstract knowledge	Earth

---

Why This Architecture

1. Biological Precedent

Animals have ultra-precise mental maps of their home range, fuzzy knowledge of distant areas. A rat knows every centimeter of its nest, vaguely knows "forest is that direction."

Young Nyx should mirror this: territory = detail.

2. Sensor Coverage Dictates Resolution

You CAN'T have 1cm resolution of Zürich — no sensors there. The resolution naturally degrades with distance from perception sources.

The nimmerhovel has 8× ESP32-S3 cameras + Pi HQ Camera. Dornach has... nothing we control.

3. S2 Cells Are Hierarchical By Design

Google's S2 geometry library already supports this:

• Level 30 ≈ 1cm cells (nimmerhovel scale)
• Level 20 ≈ 10m cells (neighborhood scale)
• Level 10 ≈ 10km cells (regional scale)

Same math, different zoom. We're not inventing new geometry — we're using S2 as intended, with dense coverage where we have sensors.

4. Compute Efficiency

Dense where it matters (can I reach the screwdriver?), sparse where it doesn't (where is France?).

---

Data Structure

SPATIAL_RESOLUTION_LAYERS = {
    "L0_scan_station": {
        "resolution": 0.001,      # 1mm - object surface detail
        "source": "Discovery Scan Station",
        "coverage": "30cm × 30cm pedestal",
        "s2_level": 30,
    },
    "L1_nimmerhovel": {
        "resolution": 0.01,       # 1cm - full 3D grid
        "source": "8× ESP32-S3 + Pi HQ Camera",
        "coverage": "Lab + Kitchen (~20m³)",
        "s2_level": 28,
        "origin": "Southwest floor corner of lab",
        "coordinate_system": "right_hand",  # Blender native
    },
    "L2_building": {
        "resolution": 0.5,        # 50cm - room-level
        "source": "Floor plans, memory",
        "coverage": "Herrenhaus",
        "s2_level": 24,
    },
    "L3_neighborhood": {
        "resolution": 10,         # 10m - landmark-level
        "source": "OpenStreetMap, walks",
        "coverage": "Dornach",
        "s2_level": 20,
    },
    "L4_region": {
        "resolution": 1000,       # 1km - city-level
        "source": "Maps, general knowledge",
        "coverage": "Switzerland",
        "s2_level": 14,
    },
    "L5_world": {
        "resolution": 100000,     # 100km - country-level
        "source": "Abstract knowledge",
        "coverage": "Earth",
        "s2_level": 8,
    },
}

---

Query Examples

Question	Layer	Response Type
"Where is the soldering iron?"	L1	Precise coordinates (2.10, 1.50, 0.85)
"Which room is the printer in?"	L2	Room name + relative position
"How do I get to Basel?"	L3/L4	Route abstraction, directions
"Where is Japan relative to here?"	L5	Directional only, abstract

---

Connection to Other Systems

Concept Token Pairs (Spatial Grounding)

The Resolution Gradient provides the coordinate system for grounded concept pairs:

• <HERE> ↔ <THERE> becomes measurable distance in L1 grid
• <NEAR> ↔ <FAR> calibrated against actual spatial distances
• Predictions have coordinates; outcomes have coordinates; delta is measurable

Artifact Data Model

Artifacts (plans, drawings, specs) exist at different resolution layers:

• L0: Object scan embeddings (sub-mm detail)
• L1: Inventory items with (X,Y,Z) positions
• L2+: Abstract references, not spatially precise

Camera Frustum Mapping

Each camera's FOV is a frustum (3D cone) that intersects L1 grid cells:

• Coverage = union of all frustums
• Blind spots = L1 cells with no frustum intersection
• Object at (X,Y,Z) → which cameras see it? At what pixels?

---

Embedding Enrichment: The Bridge to Semantic Cognition

Added: 2026-01-01 (New Year's session continuation)

The Resolution Gradient defines geometry. But geometry alone is not cognition. Each LOD level must be enriched with embeddings — semantic vectors that encode meaning, not just position.

The Technology Convergence

GAME ENGINES              S2 CELLS                 T5GEMMA2/SigLIP
────────────              ────────                 ───────────────
LOD streaming             Hierarchical cells       Vision → embeddings
Frustum culling           Spatial indexing         Semantic vectors
Texture mipmaps           Multi-resolution         Scale-invariant
Chunk loading             Cell neighbors           Context-aware

            ╲                 │                 ╱
             ╲                │                ╱
              ╲               │               ╱
               ╲              │              ╱
                ╲             │             ╱
                 ▼            ▼            ▼
         ┌─────────────────────────────────────┐
         │   EMBEDDING-ENRICHED SPATIAL LOD    │
         │                                     │
         │   Each S2 cell at each level has:   │
         │   - Geometry (game engine mesh)     │
         │   - Embeddings (SigLIP vectors)     │
         │   - Semantic density ∝ resolution   │
         └─────────────────────────────────────┘

Embedding Density Per LOD Level

Level	Geometry LOD	Embedding Density	What's Encoded
L0	Sub-mm mesh	Dense (per-surface)	Texture, material, wear patterns, defects
L1	1cm voxels	Per-object	Object identity, state, relationships
L2	Room boxes	Per-room	Room function, contents summary, atmosphere
L3	Landmarks	Per-landmark	Place identity, routes, significance
L4	Regions	Sparse	Cultural, climate, abstract properties
L5	Continents	Minimal	Directional, conceptual only

Semantic Mipmaps

Just as textures have mipmaps (pre-computed lower resolutions), embeddings can have semantic mipmaps:

L0: embedding(screwdriver_surface_detail)
     │
     ▼ aggregate
L1: embedding(screwdriver) = summary of all L0 embeddings
     │
     ▼ aggregate
L2: embedding(crafting_table_contents) = summary of all L1 objects on table
     │
     ▼ aggregate
L3: embedding(nimmerhovel_lab) = summary of all L2 areas

Query the summary first, drill down if needed. Attention = resolution selection.

The Capture Pipeline

CAPTURE                    PROCESS                   STORE
───────                    ───────                   ─────
Photo of screwdriver       SigLIP → embedding        L0 cell enriched
       │                          │                        │
Photo of crafting table    SigLIP → embedding        L1 cell enriched
       │                          │                        │
Photo of lab               SigLIP → embedding        L2 cell enriched
       │                          │                        │
Photo from window          SigLIP → embedding        L3 cell enriched

Same encoder (T5Gemma2/SigLIP), different scale.
Embeddings NEST into LOD hierarchy.

Embedding-Aware LOD Streaming

Game engines stream geometry based on camera position. We stream semantics based on attention:

def query_spatial(position, attention_radius):
    """
    Load embeddings based on attention focus -
    like game engine LOD but for SEMANTICS
    """
    cells_to_load = []

    for distance in range(0, MAX_DISTANCE):
        s2_level = distance_to_s2_level(distance)
        cells = get_s2_cells(position, distance, s2_level)

        for cell in cells:
            if distance < attention_radius:
                # HIGH ATTENTION: Load dense embeddings
                cell.load_embeddings(density="full")
                cell.load_geometry(lod="high")
            else:
                # LOW ATTENTION: Abstract embeddings only
                cell.load_embeddings(density="summary")
                cell.load_geometry(lod="low")  # or none

        cells_to_load.extend(cells)

    return cells_to_load

Why This Matters

1. Attention = Resolution: Like foveal vision (sharp center, blurry periphery), Young Nyx has foveal COGNITION — dense embeddings where attention focuses, sparse elsewhere.

2. Streaming Not Loading: Don't load the whole world. Stream embeddings based on task needs. Approaching crafting table? Stream L0/L1. Walking to Basel? L3/L4 is enough.

3. Memory Hierarchy Match: GPU VRAM is precious. The right embeddings in fast memory — detailed for nearby, abstract for distant.

4. Same Encoder, All Scales: SigLIP doesn't care if it's encoding a screw or a city. The embedding space is unified; only the source resolution varies.

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Implementation Sequence

1. Blender room shell (CURRENT - in progress)
       │
       ▼
2. Define origin point + axis alignment in Blender
       │
       ▼
3. Create L1 3D grid overlay (1cm resolution)
       │
       ▼
4. Physical anchor markers (QR codes / ArUco)
       │
       ▼
5. Camera frustum mapping against grid
       │
       ▼
6. Spatial embeddings with L1 coordinates
       │
       ▼
7. Expand outward: L2 (building), L3 (neighborhood)...

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The Promise

"The farther we go out from our lab, the more we have to abstract."

This isn't a limitation — it's wisdom. Full resolution everywhere is:

• Impossible (no sensors)
• Expensive (compute, storage)
• Unnecessary (don't need 1cm precision for "where is France")

The nimmerhovel is the high-fidelity anchor from which all spatial reasoning radiates with graceful degradation.

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Created: 2026-01-01 Philosophy: "Start where you can measure. Abstract where you must."

🗺️🔬 The world radiates from home.