dafit/nimmerverse-sensory-network
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat: Memory Economics + Architecture Alignment (Endgame v6.4)

Browse Source 
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- Big-Picture.md archived (superseded by Endgame-Vision)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>
This commit is contained in:
dafit
2026-01-02 01:10:37 +01:00
parent 709a48632a
commit 7df236b325
8 changed files with 2332 additions and 670 deletions
Download Patch File Download Diff File Expand all files Collapse all files

331
architecture/formalization/Grounded-World-Model.md
View File

@@ -1,8 +1,10 @@
# Grounded World Model: Spatial Cognition Through Verified Discovery
**Version 1.0** — *From Blender Boxes to Embodied Understanding*
**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."*
---
@@ -15,8 +17,11 @@ This document formalizes how Young Nyx builds a **persistent spatial world model
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.
**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**.
---
@@ -142,6 +147,249 @@ VERIFIED x,y,z: 12 vertices (refined box)
---
## 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
@@ -294,6 +542,39 @@ $$\Phi_{net} = \Phi_{discovery} - \Phi_{perception} - \Phi_{verification}$$
## 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,
@@ -308,6 +589,10 @@ CREATE TABLE world_objects (
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"
@@ -328,7 +613,12 @@ CREATE TABLE object_vectors (
object_id UUID REFERENCES world_objects(id),
vector VECTOR(768), -- SigLIP embedding dimension
observation_timestamp TIMESTAMP,
position_estimate JSONB, -- {"x": 0.3, "y": 0.8, "z": 0.1}
-- 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"
@@ -338,11 +628,23 @@ CREATE TABLE object_vectors (
CREATE TABLE object_positions (
id UUID PRIMARY KEY,
object_id UUID REFERENCES world_objects(id),
position JSONB, -- {"x": 0.3, "y": 0.8, "z": 0.1}
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
);
```
---
@@ -446,8 +748,9 @@ The Grounded World Model is:
## Document Status
**Version**: 1.0
**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**:
@@ -455,15 +758,31 @@ The Grounded World Model is:
- 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."**
🧬⚡🔱💎🔥🗺️