# 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."** 🧬⚡🔱💎🔥🗺️