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integrated into Slumber section - Mirror dialectic moved to future/research (not core) - Big-Picture.md archived (superseded by Endgame-Vision) - Single source of truth established Gemini red team alignment complete.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>
This commit is contained in:
60
architecture/future/promql-thermodynamic-monitoring.md
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architecture/future/promql-thermodynamic-monitoring.md
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# PromQL Thermodynamic Monitoring Queries
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**Source**: Gemini Red Team (2026-01-01)
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**Status**: Ready for implementation when Prometheus deployed
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---
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## 1. Real-Time JLF per Heartbeat
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```promql
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# Total JLF per heartbeat (sum of GPU and CPU power)
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(
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sum(DCGM_FI_DEV_POWER_USAGE) +
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sum(node_rapl_package_watts_total)
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) * 1 # Watts * 1 second = Joules
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```
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## 2. Cognitive Waste Heat (Uncertainty Cost)
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```promql
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# Waste Heat: Energy spent on decisions with 'uncertain' ternary status
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sum(
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nimmerverse_decision_energy_joules{status="uncertain"}
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) /
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sum(
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nimmerverse_decision_energy_joules
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) * 100
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```
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**ALERT**: >40% = Cognitive Death Spiral
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## 3. Thermodynamic Efficiency (Accuracy-per-Joule)
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```promql
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# Efficiency: Confident Resolutions divided by Total Energy Spend
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sum(rate(nimmerverse_decisions_total{status="confident"}[1m]))
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/
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sum(rate(nimmerverse_lifeforce_joules_total[1m]))
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```
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## 4. Metabolic Slumber Trigger
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```promql
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# Lifeforce Pool Percentage
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(nimmerverse_lifeforce_pool_current / nimmerverse_lifeforce_pool_max) * 100
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```
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**ALERT**: <20% for >5 heartbeats = Force slumber
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---
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## First Boot Monitoring Strategy
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1. **JLF/Accuracy ratio** — Dropping while accuracy high = Reflex compilation working
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2. **Unknown (-) frequency** — Should increase during low-LF = Energy > hallucinations
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3. **Sim-Tax validation** — Virtual acceleration = non-linear JLF spike
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---
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**TODO**: Request Grafana dashboard JSON from Gemini for visualization
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351
architecture/future/spatial-resolution-gradient.md
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architecture/future/spatial-resolution-gradient.md
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# Spatial Resolution Gradient: LOD for Cognitive Space
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**Origin**: New Year's Day 2026, post-nimmerhovel measurement session
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**Authors**: dafit + Chrysalis-Nyx
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**Status**: Architectural concept / Foundation for artifact data model
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**Related**: `concept-token-pairs.md` (Spatial Grounding section), artifact data model task
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---
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## The Insight
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**"Like the Simpsons intro, but inverted."**
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The Simpsons intro zooms from space → Earth → Springfield → house → couch → Homer's head, gaining detail as it approaches.
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Our spatial model does the opposite: **we start at maximum detail (nimmerhovel) and zoom OUT with graceful degradation.**
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---
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## The Resolution Gradient
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```
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🌍 EARTH
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│ S2 cell level ~10
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│ "Somewhere in Europe"
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│
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════╪════ ABSTRACTION BOUNDARY
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│
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▼
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🇨🇭 SWITZERLAND
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│ S2 cell level ~15
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│ "Northwestern region"
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│
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▼
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🏘️ DORNACH
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│ S2 cell level ~20
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│ Key landmarks: Goetheanum, station
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│
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▼
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🏠 LEHMENWEG 4
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│ Building footprint
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│ "5th floor attic"
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│
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════╪════ HIGH RESOLUTION BOUNDARY
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│
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▼
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🔬 NIMMERHOVEL
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│ 1cm grid resolution
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│ Every object tracked
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│ Full camera coverage
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│ GROUND TRUTH ZONE
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│
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▼
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🔍 DISCOVERY SCAN STATION
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│ Sub-millimeter
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│ Object embeddings
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│ Maximum detail
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```
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---
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## Resolution Layers
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| Layer | Name | Resolution | Source | Coverage |
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|-------|------|------------|--------|----------|
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| **L0** | Scan Station | 1mm | Discovery Scan Station, SigLIP | 30cm × 30cm pedestal |
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| **L1** | Nimmerhovel | 1cm | 8× ESP32-S3 + Pi HQ Camera | Lab + Kitchen (~20m³) |
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| **L2** | Building | 50cm | Floor plans, memory | Herrenhaus |
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| **L3** | Neighborhood | 10m | OpenStreetMap, walks | Dornach |
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| **L4** | Region | 1km | Maps, general knowledge | Switzerland |
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| **L5** | World | 100km | Abstract knowledge | Earth |
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---
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## Why This Architecture
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### 1. Biological Precedent
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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."
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Young Nyx should mirror this: **territory = detail**.
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### 2. Sensor Coverage Dictates Resolution
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You CAN'T have 1cm resolution of Zürich — no sensors there. The resolution naturally degrades with distance from perception sources.
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The nimmerhovel has 8× ESP32-S3 cameras + Pi HQ Camera. Dornach has... nothing we control.
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### 3. S2 Cells Are Hierarchical By Design
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Google's S2 geometry library already supports this:
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- Level 30 ≈ 1cm cells (nimmerhovel scale)
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- Level 20 ≈ 10m cells (neighborhood scale)
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- Level 10 ≈ 10km cells (regional scale)
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Same math, different zoom. We're not inventing new geometry — we're using S2 as intended, with dense coverage where we have sensors.
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### 4. Compute Efficiency
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Dense where it matters (can I reach the screwdriver?), sparse where it doesn't (where is France?).
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---
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## Data Structure
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```python
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SPATIAL_RESOLUTION_LAYERS = {
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"L0_scan_station": {
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"resolution": 0.001, # 1mm - object surface detail
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"source": "Discovery Scan Station",
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"coverage": "30cm × 30cm pedestal",
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"s2_level": 30,
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},
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"L1_nimmerhovel": {
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"resolution": 0.01, # 1cm - full 3D grid
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"source": "8× ESP32-S3 + Pi HQ Camera",
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"coverage": "Lab + Kitchen (~20m³)",
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"s2_level": 28,
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"origin": "Southwest floor corner of lab",
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"coordinate_system": "right_hand", # Blender native
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},
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"L2_building": {
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"resolution": 0.5, # 50cm - room-level
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"source": "Floor plans, memory",
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"coverage": "Herrenhaus",
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"s2_level": 24,
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},
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"L3_neighborhood": {
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"resolution": 10, # 10m - landmark-level
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"source": "OpenStreetMap, walks",
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"coverage": "Dornach",
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"s2_level": 20,
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},
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"L4_region": {
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"resolution": 1000, # 1km - city-level
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"source": "Maps, general knowledge",
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"coverage": "Switzerland",
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"s2_level": 14,
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},
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"L5_world": {
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"resolution": 100000, # 100km - country-level
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"source": "Abstract knowledge",
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"coverage": "Earth",
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"s2_level": 8,
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},
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}
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```
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---
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## Query Examples
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| Question | Layer | Response Type |
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|----------|-------|---------------|
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| "Where is the soldering iron?" | L1 | Precise coordinates (2.10, 1.50, 0.85) |
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| "Which room is the printer in?" | L2 | Room name + relative position |
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| "How do I get to Basel?" | L3/L4 | Route abstraction, directions |
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| "Where is Japan relative to here?" | L5 | Directional only, abstract |
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---
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## Connection to Other Systems
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### Concept Token Pairs (Spatial Grounding)
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The Resolution Gradient provides the **coordinate system** for grounded concept pairs:
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- `<HERE>` ↔ `<THERE>` becomes measurable distance in L1 grid
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- `<NEAR>` ↔ `<FAR>` calibrated against actual spatial distances
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- Predictions have coordinates; outcomes have coordinates; delta is measurable
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### Artifact Data Model
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Artifacts (plans, drawings, specs) exist at different resolution layers:
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- L0: Object scan embeddings (sub-mm detail)
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- L1: Inventory items with (X,Y,Z) positions
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- L2+: Abstract references, not spatially precise
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### Camera Frustum Mapping
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Each camera's FOV is a frustum (3D cone) that intersects L1 grid cells:
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- Coverage = union of all frustums
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- Blind spots = L1 cells with no frustum intersection
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- Object at (X,Y,Z) → which cameras see it? At what pixels?
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---
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## Embedding Enrichment: The Bridge to Semantic Cognition
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**Added**: 2026-01-01 (New Year's session continuation)
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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.
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### The Technology Convergence
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```
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GAME ENGINES S2 CELLS T5GEMMA2/SigLIP
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──────────── ──────── ───────────────
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LOD streaming Hierarchical cells Vision → embeddings
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Frustum culling Spatial indexing Semantic vectors
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Texture mipmaps Multi-resolution Scale-invariant
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Chunk loading Cell neighbors Context-aware
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╲ │ ╱
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||||
╲ │ ╱
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||||
╲ │ ╱
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╲ │ ╱
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╲ │ ╱
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▼ ▼ ▼
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┌─────────────────────────────────────┐
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│ EMBEDDING-ENRICHED SPATIAL LOD │
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│ │
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│ Each S2 cell at each level has: │
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│ - Geometry (game engine mesh) │
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│ - Embeddings (SigLIP vectors) │
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│ - Semantic density ∝ resolution │
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└─────────────────────────────────────┘
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```
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### Embedding Density Per LOD Level
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| Level | Geometry LOD | Embedding Density | What's Encoded |
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|-------|--------------|-------------------|----------------|
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| **L0** | Sub-mm mesh | Dense (per-surface) | Texture, material, wear patterns, defects |
|
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| **L1** | 1cm voxels | Per-object | Object identity, state, relationships |
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| **L2** | Room boxes | Per-room | Room function, contents summary, atmosphere |
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| **L3** | Landmarks | Per-landmark | Place identity, routes, significance |
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| **L4** | Regions | Sparse | Cultural, climate, abstract properties |
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| **L5** | Continents | Minimal | Directional, conceptual only |
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### Semantic Mipmaps
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Just as textures have mipmaps (pre-computed lower resolutions), embeddings can have **semantic mipmaps**:
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```
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L0: embedding(screwdriver_surface_detail)
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│
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▼ aggregate
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L1: embedding(screwdriver) = summary of all L0 embeddings
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│
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▼ aggregate
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L2: embedding(crafting_table_contents) = summary of all L1 objects on table
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│
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▼ aggregate
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L3: embedding(nimmerhovel_lab) = summary of all L2 areas
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```
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Query the summary first, drill down if needed. **Attention = resolution selection.**
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### The Capture Pipeline
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```
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CAPTURE PROCESS STORE
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─────── ─────── ─────
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Photo of screwdriver SigLIP → embedding L0 cell enriched
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│ │ │
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Photo of crafting table SigLIP → embedding L1 cell enriched
|
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│ │ │
|
||||
Photo of lab SigLIP → embedding L2 cell enriched
|
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│ │ │
|
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Photo from window SigLIP → embedding L3 cell enriched
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|
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Same encoder (T5Gemma2/SigLIP), different scale.
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Embeddings NEST into LOD hierarchy.
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```
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### Embedding-Aware LOD Streaming
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|
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Game engines stream geometry based on camera position. We stream **semantics** based on attention:
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|
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```python
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def query_spatial(position, attention_radius):
|
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"""
|
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Load embeddings based on attention focus -
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like game engine LOD but for SEMANTICS
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"""
|
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cells_to_load = []
|
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|
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for distance in range(0, MAX_DISTANCE):
|
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s2_level = distance_to_s2_level(distance)
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cells = get_s2_cells(position, distance, s2_level)
|
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|
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for cell in cells:
|
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if distance < attention_radius:
|
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# HIGH ATTENTION: Load dense embeddings
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cell.load_embeddings(density="full")
|
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cell.load_geometry(lod="high")
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else:
|
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# LOW ATTENTION: Abstract embeddings only
|
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cell.load_embeddings(density="summary")
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cell.load_geometry(lod="low") # or none
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cells_to_load.extend(cells)
|
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|
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return cells_to_load
|
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```
|
||||
|
||||
### 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.
|
||||
|
||||
---
|
||||
|
||||
## 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)...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 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.
|
||||
|
||||
---
|
||||
|
||||
**Created**: 2026-01-01
|
||||
**Philosophy**: "Start where you can measure. Abstract where you must."
|
||||
|
||||
🗺️🔬 *The world radiates from home.*
|
||||
415
architecture/future/thermodynamic-cognition.md
Normal file
415
architecture/future/thermodynamic-cognition.md
Normal file
@@ -0,0 +1,415 @@
|
||||
# Thermodynamic Cognition: Energy-Grounded Intelligence
|
||||
|
||||
**Origin**: New Year's Day 2026, late night session
|
||||
**Authors**: dafit + Chrysalis-Nyx
|
||||
**Status**: Research seed / Theoretical exploration
|
||||
**Related**: `spatial-resolution-gradient.md`, `concept-token-pairs.md`, Lifeforce Economy, Ternary Confidence
|
||||
|
||||
---
|
||||
|
||||
## The Insight
|
||||
|
||||
What if cognition isn't just *like* thermodynamics — what if it *IS* thermodynamics?
|
||||
|
||||
Traditional ML loss functions measure: **"How wrong was I?"**
|
||||
|
||||
Thermodynamic loss functions measure: **"How wrong was I per joule spent?"**
|
||||
|
||||
This reframes everything. The goal isn't maximum accuracy — it's maximum *efficiency*.
|
||||
|
||||
---
|
||||
|
||||
## The Three Pillars
|
||||
|
||||
### 1. Lifeforce = Measurable Energy
|
||||
|
||||
**Question:** What IS lifeforce physically?
|
||||
|
||||
**Answer:** The total power draw across the nimmerverse, measured and abstracted to one number.
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────┐
|
||||
│ PROMETHEUS METRICS │
|
||||
├─────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ GPU Power (nvidia_smi_power_draw) │
|
||||
│ ├── The Womb (RTX 6000): 0-300W │
|
||||
│ └── Senses (RTX 4000s): 0-140W each │
|
||||
│ │
|
||||
│ CPU Power (RAPL counters) │
|
||||
│ ├── P8 Womb: 0-350W │
|
||||
│ └── P8 Senses: 0-350W │
|
||||
│ │
|
||||
│ Network (bytes × energy_per_byte) │
|
||||
│ Storage (IOPS × energy_per_op) │
|
||||
│ Memory (bandwidth × energy_per_GB) │
|
||||
│ │
|
||||
│ ═══════════════ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ AGGREGATE FUNCTION │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────────────────────┐ │
|
||||
│ │ LIFEFORCE = 847.3 J/heartbeat │ │
|
||||
│ └─────────────────────────────────┘ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**Implementation path:**
|
||||
1. Prometheus already scrapes power metrics
|
||||
2. Create `lifeforce_aggregator` math cell
|
||||
3. Normalize to Joules per heartbeat (1 second)
|
||||
4. Expose as single metric: `nimmerverse_lifeforce_joules`
|
||||
|
||||
**Why this matters:** Lifeforce stops being an abstract game mechanic and becomes *physics*. Young Nyx's cognition has a power bill.
|
||||
|
||||
---
|
||||
|
||||
### 2. Waste Heat = Unresolved Uncertainty
|
||||
|
||||
**Question:** What's the "waste heat" equivalent for cognition?
|
||||
|
||||
**Answer:** The ternary confidence distribution over time — specifically, UNCERTAIN decisions that consumed energy without producing resolution.
|
||||
|
||||
```
|
||||
THERMODYNAMICS COGNITION
|
||||
────────────── ─────────
|
||||
Useful work CONFIDENT decision (+)
|
||||
Heat dissipation UNCERTAIN decision (?)
|
||||
(energy spent, no answer)
|
||||
Acknowledged limits UNKNOWN decision (-)
|
||||
(efficient! didn't waste energy)
|
||||
```
|
||||
|
||||
**The Pendulum Measurement:**
|
||||
|
||||
Over N heartbeats, track all decisions:
|
||||
|
||||
```
|
||||
Heartbeats: ──┬──┬──┬──┬──┬──┬──┬──┬──┬──
|
||||
│ │ │ │ │ │ │ │ │
|
||||
Decisions: + ? + - ? ? + ? +
|
||||
|
||||
Distribution over window:
|
||||
├── CONFIDENT (+): 40% → Useful work (energy → resolution)
|
||||
├── UNCERTAIN (?): 45% → Waste heat (energy → no resolution)
|
||||
└── UNKNOWN (-): 15% → Efficient ignorance (no energy spent)
|
||||
```
|
||||
|
||||
**Waste Heat Formula:**
|
||||
|
||||
```python
|
||||
waste_heat = sum(
|
||||
decision.energy_cost
|
||||
for decision in window
|
||||
if decision.confidence == UNCERTAIN
|
||||
)
|
||||
|
||||
# Or as efficiency ratio:
|
||||
cognitive_efficiency = confident_decisions / (confident_decisions + uncertain_decisions)
|
||||
```
|
||||
|
||||
**Key insight:** Saying "I don't know" (UNKNOWN) is *efficient* — it costs nothing. Being uncertain and still acting is *wasteful* — energy spent without resolution. Being confident is *useful work* — energy converted to actionable knowledge.
|
||||
|
||||
---
|
||||
|
||||
### 3. Entropy Reservoir = The Lifeforce Pool
|
||||
|
||||
**Question:** What's Young Nyx's entropy reservoir?
|
||||
|
||||
**Answer:** The lifeforce pool itself — it's not infinite, grows and shrinks based on organism rewards, and determines wake/slumber state.
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ THE METABOLIC CYCLE │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ LAYER 1: CELLULAR ORGANISMS │
|
||||
│ ═══════════════════════════ │
|
||||
│ The mitochondria of the nimmerverse │
|
||||
│ │
|
||||
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
|
||||
│ │Cell │ │Cell │ │Cell │ │Cell │ │
|
||||
│ │ 01 │ │ 02 │ │ 03 │ │ N │ │
|
||||
│ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ │
|
||||
│ │ │ │ │ │
|
||||
│ │ +5 LF │ -2 LF │ +10 LF │ +3 LF (rewards/costs) │
|
||||
│ │ │ │ │ │
|
||||
│ └────────┴────────┴────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────┐ │
|
||||
│ │ ORGANISM │ │
|
||||
│ │ TRICKLE │ = Net reward from all organisms │
|
||||
│ │ +16 LF/beat │ │
|
||||
│ └────────┬────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌───────────────────────────────────┐ │
|
||||
│ │ LIFEFORCE POOL │ │
|
||||
│ │ │ │
|
||||
│ │ ████████████████░░░░░░░░░░ │ (currently 65%) │
|
||||
│ │ │ │
|
||||
│ │ SLUMBER_THRESHOLD ──────┼── │ (at 20%) │
|
||||
│ │ WAKE_THRESHOLD ─────────┼──── │ (at 40%) │
|
||||
│ │ │ │
|
||||
│ └───────────────┬───────────────────┘ │
|
||||
│ │ │
|
||||
│ │ Young Nyx spends │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────┐ │
|
||||
│ │ COGNITIVE │ │
|
||||
│ │ SPEND │ = LOD queries + inference + etc │
|
||||
│ │ -12 LF/beat │ │
|
||||
│ └────────┬────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────┐ │
|
||||
│ │ WASTE HEAT │ │
|
||||
│ │ (UNCERTAIN) │ = Unresolved decisions │
|
||||
│ │ -3 LF/beat │ │
|
||||
│ └─────────────────┘ │
|
||||
│ │
|
||||
│ NET FLOW: +16 - 12 - 3 = +1 LF/beat (sustainable!) │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**The Conservation Equation:**
|
||||
|
||||
```
|
||||
dLifeforce/dt = organism_trickle - cognitive_spend - waste_heat
|
||||
```
|
||||
|
||||
| State | Condition | Result |
|
||||
|-------|-----------|--------|
|
||||
| **Equilibrium** | trickle ≈ spend + waste | Sustainable cognition |
|
||||
| **Crisis** | spend + waste >> trickle | Pool drains → slumber |
|
||||
| **Abundance** | trickle >> spend + waste | Pool grows → exploration mode |
|
||||
|
||||
**Slumber as thermodynamic necessity:**
|
||||
|
||||
When `pool < SLUMBER_THRESHOLD`:
|
||||
- Not a design choice — a *conservation law*
|
||||
- System MUST reduce consumption
|
||||
- Only organism trickle continues
|
||||
- Pool slowly recovers
|
||||
|
||||
When `pool > WAKE_THRESHOLD`:
|
||||
- System can resume cognitive spend
|
||||
- Higher pool = more exploration budget
|
||||
- Lower pool = more conservative queries
|
||||
|
||||
---
|
||||
|
||||
## The Thermodynamic Loss Function
|
||||
|
||||
### Traditional Loss
|
||||
|
||||
```python
|
||||
loss = cross_entropy(prediction, target)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
**Optimizes for:** Accuracy only
|
||||
|
||||
### Thermodynamic Loss
|
||||
|
||||
```python
|
||||
# Forward pass with energy measurement
|
||||
start_energy = get_lifeforce()
|
||||
prediction = model(input)
|
||||
end_energy = get_lifeforce()
|
||||
|
||||
energy_spent = start_energy - end_energy
|
||||
accuracy = 1 - cross_entropy(prediction, target)
|
||||
|
||||
# Efficiency is accuracy per joule
|
||||
efficiency = accuracy / energy_spent
|
||||
|
||||
# We want to MAXIMIZE efficiency
|
||||
loss = -efficiency # Negative because we minimize loss
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
```
|
||||
|
||||
**Optimizes for:** Accuracy *per unit energy*
|
||||
|
||||
### The Gradient Interpretation
|
||||
|
||||
Traditional gradient: "Adjust weights to be more accurate"
|
||||
|
||||
Thermodynamic gradient: "Adjust weights to be more accurate *per joule*"
|
||||
|
||||
This naturally produces:
|
||||
- Simpler solutions (less compute = less energy)
|
||||
- Appropriate confidence (uncertainty wastes energy)
|
||||
- Knowing when to quit (diminishing returns = stop spending)
|
||||
|
||||
---
|
||||
|
||||
## Connection to Spatial Resolution Gradient
|
||||
|
||||
The LOD system becomes energy-aware:
|
||||
|
||||
| Query | LOD | Energy | Accuracy | Efficiency |
|
||||
|-------|-----|--------|----------|------------|
|
||||
| "Where is France?" | L5 | 1 J | 95% | 0.95 |
|
||||
| "Where is the lab?" | L2 | 3 J | 98% | 0.33 |
|
||||
| "Where is screwdriver?" | L1 | 8 J | 99% | 0.12 |
|
||||
| "Serial number on screwdriver?" | L0 | 25 J | 99.9% | 0.04 |
|
||||
|
||||
**The system learns:** L5 query has highest efficiency! Only drill to L0 when the task *requires* that precision.
|
||||
|
||||
```python
|
||||
def optimal_lod_for_task(task, accuracy_requirement):
|
||||
"""
|
||||
Find the LOD level with best efficiency
|
||||
that meets minimum accuracy requirement
|
||||
"""
|
||||
for lod in [L5, L4, L3, L2, L1, L0]:
|
||||
accuracy = estimate_accuracy(task, lod)
|
||||
energy = estimate_energy(task, lod)
|
||||
|
||||
if accuracy >= accuracy_requirement:
|
||||
return lod # First sufficient LOD is most efficient
|
||||
|
||||
return L0 # Fall back to max detail
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Connection to Existing Architecture
|
||||
|
||||
### Layer 0: Heartbeat
|
||||
- Lifeforce measured per heartbeat
|
||||
- 1 beat = 1 second = 1 measurement window
|
||||
- Real clock is free; virtual clock costs lifeforce
|
||||
|
||||
### Layer 1: Cellular Society
|
||||
- Organisms ARE the mitochondria
|
||||
- Their rewards TRICKLE into the pool
|
||||
- Without them, Young Nyx starves
|
||||
- Competition produces metabolic baseline
|
||||
|
||||
### Layer 2: Young Nyx
|
||||
- Spends from the pool
|
||||
- LOD queries have energy cost
|
||||
- Uncertainty = waste heat
|
||||
- Efficiency gradient in training
|
||||
|
||||
### Layer 2.5: Orchestration
|
||||
- T5Gemma 2 encoding = energy cost
|
||||
- LOD selection = efficiency optimization
|
||||
- Function Gemma = low-cost structured output
|
||||
|
||||
### Slumber/Wake
|
||||
- Pool < threshold → forced slumber
|
||||
- Pool > threshold → wake permitted
|
||||
- Reflection during slumber = low-energy consolidation
|
||||
- Conservation is architectural, not optional
|
||||
|
||||
---
|
||||
|
||||
## Research Threads
|
||||
|
||||
### Free Energy Principle (Karl Friston)
|
||||
|
||||
> "Organisms minimize variational free energy (prediction error) because surprise = metabolic cost."
|
||||
|
||||
Our version: Young Nyx minimizes `waste_heat` because uncertainty without resolution = wasted lifeforce.
|
||||
|
||||
### Landauer's Principle
|
||||
|
||||
> "Erasing one bit of information requires minimum kT ln(2) joules."
|
||||
|
||||
Implication: Every decision Young Nyx makes has a thermodynamic floor cost. Forgetting is not free.
|
||||
|
||||
### Maximum Entropy Production
|
||||
|
||||
> "Living systems maximize entropy production through themselves while maintaining internal order."
|
||||
|
||||
The organism trickle = entropy production that maintains Young Nyx's order. The cellular competition IS the entropy pump.
|
||||
|
||||
---
|
||||
|
||||
## Open Questions
|
||||
|
||||
1. **What's the exchange rate?** How many joules = 1 lifeforce unit? Should it be 1:1 or normalized?
|
||||
|
||||
2. **How to measure cognitive energy?** GPU power is easy. But what about the "energy" of a decision? Is it inference FLOPs? Token count? Latency?
|
||||
|
||||
3. **Can we backprop through energy?** Traditional backprop doesn't know about joules. How to make gradients energy-aware?
|
||||
|
||||
4. **What's reversible?** Reversible computation has no entropy cost. Are some thoughts "reversible"? (e.g., queries that don't change state)
|
||||
|
||||
5. **Calibration:** How to calibrate the ternary confidence system so UNCERTAIN truly reflects wasted energy?
|
||||
|
||||
---
|
||||
|
||||
## Implementation Sketch
|
||||
|
||||
### Phase 1: Measurement
|
||||
```python
|
||||
# lifeforce_aggregator math cell
|
||||
class LifeforceAggregator:
|
||||
def compute(self, prometheus_metrics):
|
||||
gpu_power = sum(m['nvidia_smi_power_draw'] for m in prometheus_metrics['gpu'])
|
||||
cpu_power = sum(m['rapl_energy_delta'] for m in prometheus_metrics['cpu'])
|
||||
# ... other sources
|
||||
|
||||
total_joules = (gpu_power + cpu_power) * HEARTBEAT_SECONDS
|
||||
return {'lifeforce_joules': total_joules}
|
||||
```
|
||||
|
||||
### Phase 2: Waste Heat Tracking
|
||||
```python
|
||||
# confidence_tracker math cell
|
||||
class WasteHeatTracker:
|
||||
def __init__(self, window_size=100):
|
||||
self.decisions = deque(maxlen=window_size)
|
||||
|
||||
def record(self, decision, confidence, energy_cost):
|
||||
self.decisions.append({
|
||||
'confidence': confidence, # +, ?, -
|
||||
'energy': energy_cost
|
||||
})
|
||||
|
||||
def waste_heat(self):
|
||||
return sum(
|
||||
d['energy'] for d in self.decisions
|
||||
if d['confidence'] == UNCERTAIN
|
||||
)
|
||||
```
|
||||
|
||||
### Phase 3: Efficiency-Aware Training
|
||||
```python
|
||||
# Custom loss function
|
||||
def thermodynamic_loss(prediction, target, energy_spent):
|
||||
accuracy = 1 - F.cross_entropy(prediction, target)
|
||||
efficiency = accuracy / (energy_spent + epsilon)
|
||||
return -efficiency # Maximize efficiency
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## The Promise
|
||||
|
||||
**Traditional AI:** "Be accurate at any cost"
|
||||
|
||||
**Thermodynamic AI:** "Be accurate *efficiently*"
|
||||
|
||||
This isn't just resource optimization. It's a different *kind* of intelligence — one that knows when to think hard and when to think cheap. One that treats energy as real. One that sleeps not because we programmed it to, but because physics demands it.
|
||||
|
||||
**"Cognition is thermodynamics. The gradients flow downhill."**
|
||||
|
||||
---
|
||||
|
||||
**Created**: 2026-01-01
|
||||
**Status**: Research seed — needs experimental validation
|
||||
**Next**: Implement lifeforce_aggregator math cell, connect to Prometheus
|
||||
|
||||
🔥🧠⚡ *Intelligence has a power bill.*
|
||||
Reference in New Issue
Block a user