feat: Concept Token Pairs + Spatial Grounding (Silvester/New Year sessions)
Major additions from Silvester 2025 and New Year 2026 sessions: Concept Token Pairs (architecture/future/concept-token-pairs.md): - Theoretical paper on navigable reasoning spaces - Opposites create axes, not just mode switches - "Punkt vor Strich" for AI reasoning - Escape velocity from degeneration loops - NEW: Spatial Grounding section linking to physical nimmerhovel Architecture updates: - Endgame-Vision.md: v6.2 alignment - Big-Picture.md: v5.2 alignment - Modular-Organism-Design.md: conical interlocking mechanism New files: - SEEDS.md: Research seeds for future exploration - Temporal-Firework-Visualization.md: Temporal data viz concept Key insight from 2026-01-01 session: "Don't train the answer. Train the space where answers live." → "Don't imagine the space. MEASURE it." Spatial embeddings from nimmerhovel hardware (8× ESP32-S3 AI CAM, Pi HQ Camera, Discovery Scan Station) can ground concept pairs in physical reality, not just symbolic patterns. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
153
architecture/future/SEEDS.md
Normal file
153
architecture/future/SEEDS.md
Normal file
@@ -0,0 +1,153 @@
|
||||
# Seeds
|
||||
|
||||
**Future possibilities we're building toward but not speccing yet.**
|
||||
|
||||
These are nuggets - insights that emerged from sessions, not fully designed, but worth remembering so we don't re-discover them later.
|
||||
|
||||
---
|
||||
|
||||
## Counterfactual Training via Time Machine
|
||||
**Origin**: Silvester 2025, fireworks over Basel
|
||||
**Seed**: The temporal visualization isn't just for debugging - it's training infrastructure.
|
||||
|
||||
Run multiple synthetic decision variants against historical data. Compare to ground truth (what actually happened). Fold winning weights back into live model. The time machine becomes perpetual training fuel.
|
||||
|
||||
**Enables**:
|
||||
- Offline RL from logged events
|
||||
- "What if?" exploration without new data
|
||||
- Dialectic between live Nyx and all possible Nyxes
|
||||
|
||||
**Requires**: Rich metadata (✓ building), S2+timestamp indexing (✓ building), cheap local inference (ThinkStation coming)
|
||||
|
||||
---
|
||||
|
||||
## LoRa Mesh Over Jura Hilltops
|
||||
**Origin**: Silvester 2025, bus ride from Liestal
|
||||
**Seed**: Line of sight from Hovel → Aesch tower → Gempen → Liestal Aussichtsturm.
|
||||
|
||||
Amateur radio license + BACOM registration (50 CHF) → access to Swiss federal LoRa grid. Wild sensor mesh spanning the hillside.
|
||||
|
||||
**Enables**:
|
||||
- Environmental sensing beyond garden walls
|
||||
- Migration tracking, weather correlation
|
||||
- Nimmerverse expanding into the physical landscape
|
||||
|
||||
**Requires**: BACOM registration, LoRa hardware, tower access permissions
|
||||
|
||||
---
|
||||
|
||||
## Corvid Behavioral Prediction as Training Ground
|
||||
**Origin**: Silvester 2025, 5 years of cigarette-break phenology
|
||||
**Seed**: Magpie nut-cracking ritual is multi-stage, predictable, perfect for temporal prediction training.
|
||||
|
||||
Nut pickup → flight to Flachdach → bussard check → fly to Christmas-light house → drop on street → crack → eat on roof → shell bashing → raven conflict.
|
||||
|
||||
Each stage is a prediction target. Rich enough for serious ML, visible from lab window.
|
||||
|
||||
**Enables**:
|
||||
- Real behavioral sequences for vision model training
|
||||
- Temporal prediction benchmarks
|
||||
- Object binding across space and time (S2 cells)
|
||||
|
||||
**Requires**: Camera mount (Flachdach view), vintage Canon lens, ESP32-S3 or Pi HQ
|
||||
|
||||
---
|
||||
|
||||
## S2 as Universal Spatial Representation (Video → Training)
|
||||
**Origin**: Silvester 2025, post-fireworks insight
|
||||
**Seed**: S2 spatial indexing isn't just for live sensors - it's a universal representation for any spatial-temporal data.
|
||||
|
||||
Take a video (glass breaking, bird flying, car crash). Encode each frame into S2 cells with timestamps. Now you can:
|
||||
- Query any moment spatially
|
||||
- Generate synthetic variations (perturb positions, velocities)
|
||||
- Train models on predicting future spatial states
|
||||
- Compare predictions against ground truth frames
|
||||
|
||||
**The pattern:**
|
||||
```
|
||||
Video → frame-by-frame object detection → S2 cell encoding →
|
||||
→ synthetic variations → temporal prediction training
|
||||
```
|
||||
|
||||
**Enables**:
|
||||
- Infinite training data from limited real video
|
||||
- Physics prediction without physics engine
|
||||
- Same query language for real/recorded/simulated data
|
||||
- Unified substrate: observation = replay = simulation
|
||||
|
||||
**Requires**: Object detection pipeline, S2 encoding layer, variation generator
|
||||
|
||||
**Compute optimization**: Many physics variations are linearly related (mirror, scale, rotate, time-reverse). Don't simulate each variation - simulate base cases, derive variations via transforms. 100x data for 1x compute.
|
||||
|
||||
**Related**: Counterfactual Training, Corvid Behavioral Prediction
|
||||
|
||||
---
|
||||
|
||||
## T5Gemma 2 + Function Gemma: The Vision-Action Pipeline
|
||||
**Origin**: Silvester 2025, late-night architecture insight
|
||||
**Seed**: Two models solve the entire vision-to-action automation at scale.
|
||||
|
||||
### T5Gemma 2 (Vision → Vectors)
|
||||
Encoder-decoder from Gemma 3, SigLIP vision encoder produces **semantic vectors directly** (not text descriptions). This IS the embedding - no text intermediary bottleneck.
|
||||
|
||||
| Model | Total Params | Use Case |
|
||||
|-------|--------------|----------|
|
||||
| 270M-270M | ~0.8B | Edge/lightweight senses |
|
||||
| 1B-1B | ~2B | Field deployment |
|
||||
| 4B-4B | ~9B | Central processing (RTX 6000) |
|
||||
|
||||
Key features:
|
||||
- 128K context window
|
||||
- 140+ languages (multilingual nimmerverse!)
|
||||
- Encoder produces vectors, decoder optional (only for human text)
|
||||
|
||||
### Function Gemma (Vectors → Actions)
|
||||
Structured output, function calling, executable actions. When the system needs to DO something based on vision, Function Gemma generates structured calls.
|
||||
|
||||
### The Pipeline
|
||||
|
||||
```
|
||||
Vision Organs (constant stream)
|
||||
│
|
||||
▼
|
||||
T5Gemma 2 Encoder
|
||||
(SigLIP → vectors)
|
||||
│
|
||||
├────────────────────▶ S2 + Timestamp → Iris/Phoebe
|
||||
│ (spatial storage)
|
||||
│
|
||||
▼
|
||||
Function Gemma
|
||||
(when action needed)
|
||||
│
|
||||
▼
|
||||
Structured Output
|
||||
{"action": "alert", "target": "corvid_detected", ...}
|
||||
```
|
||||
|
||||
**Enables**:
|
||||
- Massive scale vision processing without text bottleneck
|
||||
- Direct vector storage in spatial system
|
||||
- Structured, reliable action generation
|
||||
- Edge deployment (small models) + central processing (large models)
|
||||
|
||||
**Crucial interlink**: These two models together automate the full loop from seeing to storing to acting. The pipeline can "go wild" with vision data at scale.
|
||||
|
||||
**Related**: S2 Spatial Representation, Data Artifact Model, Corvid Observation
|
||||
|
||||
---
|
||||
|
||||
## How to Use This File
|
||||
|
||||
1. **Add nuggets** when insights emerge in sessions
|
||||
2. **Don't over-spec** - keep entries short, seed-like
|
||||
3. **Reference origin** - when/where the idea came from
|
||||
4. **Note what it enables** - why it matters
|
||||
5. **Note what it requires** - what foundations needed
|
||||
6. **Graduate to ADR or spec** when we're ready to build
|
||||
|
||||
---
|
||||
|
||||
**Philosophy**: *"Plant seeds. Water foundations. Harvest when ready."*
|
||||
|
||||
**Last Updated**: 2025-12-31
|
||||
455
architecture/future/concept-token-pairs.md
Normal file
455
architecture/future/concept-token-pairs.md
Normal file
@@ -0,0 +1,455 @@
|
||||
# Concept Token Pairs: Navigable Reasoning Spaces
|
||||
|
||||
**Origin**: Silvester 2025, ~25 minutes before midnight
|
||||
**Authors**: dafit + Chrysalis-Nyx
|
||||
**Status**: Theoretical exploration / Research seed
|
||||
|
||||
---
|
||||
|
||||
## The Problem
|
||||
|
||||
### Token Bottleneck
|
||||
|
||||
Current LLM architecture has a fundamental limitation:
|
||||
|
||||
```
|
||||
INPUT: Tokens (discrete symbols)
|
||||
│
|
||||
▼
|
||||
PROCESS: Weights activate based on token patterns
|
||||
│
|
||||
▼
|
||||
OUTPUT: Tokens (discrete symbols)
|
||||
```
|
||||
|
||||
**Critical thinking requires**: "Is this TRUE?"
|
||||
**What weights learned**: "Is this LIKELY given training?"
|
||||
|
||||
These are not the same thing. Semantics are scaffolding; weights are the actual driver. There's no grounding to reality in the token→token loop.
|
||||
|
||||
### The Degeneration Problem
|
||||
|
||||
When models "go off rails," they exhibit a clear pattern:
|
||||
|
||||
```
|
||||
Step 1: Reasonable claim
|
||||
Step 2: Similar reasoning
|
||||
Step 3: Same pattern
|
||||
Step 4: Same pattern ← Loop begins
|
||||
Step 5: Same pattern
|
||||
...
|
||||
```
|
||||
|
||||
**Diagnosis**: Not enough represented in the latent space at that point. The model is stuck in a local attractor with no opposing force, no "wait, I'm repeating myself," no awareness of the boundary.
|
||||
|
||||
---
|
||||
|
||||
## The Insight
|
||||
|
||||
### Latent Expansion is Too Expensive
|
||||
|
||||
True latent space exploration at runtime is computationally prohibitive. But training is offline—we have time.
|
||||
|
||||
**Key realization**: We can COMPILE reasoning patterns into tokens.
|
||||
|
||||
### Opposites Define Navigable Space
|
||||
|
||||
Single tokens create points. **Paired opposite tokens create axes.**
|
||||
|
||||
```
|
||||
SINGLE TOKEN PAIRED CONCEPT TOKENS
|
||||
──────────── ─────────────────────
|
||||
<CRITICAL> <TRUE> ←───────→ <FALSE>
|
||||
Just a mode switch Creates an AXIS
|
||||
|
||||
Where does claim X fall?
|
||||
|
||||
<TRUE>────X────────<FALSE>
|
||||
│
|
||||
▼
|
||||
"Leaning false, but not certain"
|
||||
```
|
||||
|
||||
### The Semantic Manifold
|
||||
|
||||
Multiple pairs create a coordinate system for reasoning:
|
||||
|
||||
```
|
||||
<TRUE>
|
||||
│
|
||||
│
|
||||
<CERTAIN> ────────────┼──────────── <UNCERTAIN>
|
||||
│
|
||||
│
|
||||
<FALSE>
|
||||
|
||||
A claim can be PLACED:
|
||||
- Vector position in this space
|
||||
- Not just "true/false" but WHERE in the span
|
||||
- Not just "certain/uncertain" but degree
|
||||
```
|
||||
|
||||
Core concept pairs that define reasoning dimensions:
|
||||
|
||||
| Pair | Dimension |
|
||||
|------|-----------|
|
||||
| `<TRUE>` ↔ `<FALSE>` | Veracity axis |
|
||||
| `<CERTAIN>` ↔ `<UNCERTAIN>` | Confidence axis |
|
||||
| `<SELF>` ↔ `<OTHER>` | Identity axis |
|
||||
| `<CAUSE>` ↔ `<EFFECT>` | Causality axis |
|
||||
| `<PAST>` ↔ `<FUTURE>` | Temporal axis |
|
||||
| `<HELP>` ↔ `<HARM>` | Ethics axis |
|
||||
|
||||
---
|
||||
|
||||
## The Mechanism
|
||||
|
||||
### Punkt vor Strich for Reasoning
|
||||
|
||||
In mathematics, simple rules constrain valid operations:
|
||||
- Punkt vor Strich (multiplication before addition)
|
||||
- Brackets have priority
|
||||
- Division by zero is undefined
|
||||
|
||||
**Concept token pairs create analogous rules for reasoning:**
|
||||
|
||||
```
|
||||
<OPPOSITE> vor <COLLAPSE> Check opposite before committing
|
||||
<BOUND> vor <INFINITY> Stay within defined space
|
||||
```
|
||||
|
||||
### Escape Velocity from Loops
|
||||
|
||||
```
|
||||
Without opposites: Gravity well, no escape
|
||||
●→→→→→⟳ (stuck forever)
|
||||
|
||||
With opposites: Tension between poles
|
||||
<A> ←──●──→ <B>
|
||||
Can't collapse to either
|
||||
Must find POSITION, not POLE
|
||||
```
|
||||
|
||||
The opposites create **escape velocity**:
|
||||
- If position not changing → stuck detected
|
||||
- Force movement toward opposite to escape
|
||||
- Find new equilibrium
|
||||
- Actual reasoning, not loop
|
||||
|
||||
### The Training Pipeline
|
||||
|
||||
```
|
||||
OFFLINE (training time)
|
||||
───────────────────────
|
||||
|
||||
1. MINE THE SCRATCHPAD
|
||||
- Collect decision trails, logged outcomes
|
||||
- Build token catalogue from reasoning traces
|
||||
|
||||
2. PROBE WEIGHT DISTRIBUTIONS
|
||||
- How do tokens distribute weights when reasoning well?
|
||||
- How do they distribute when reasoning poorly?
|
||||
- Find the SHAPE of "good reasoning" in weight space
|
||||
|
||||
3. DEFINE THE SPANS
|
||||
- Identify natural opposing clusters
|
||||
- Define mathematical boundaries of concept spaces
|
||||
|
||||
4. TRAIN CONCEPT TOKEN PAIRS
|
||||
- Create <CONCEPT> token that activates region X
|
||||
- Create <ANTI-CONCEPT> token that activates opposite region
|
||||
- Train them to maintain tension/distance
|
||||
|
||||
5. VALIDATE NAVIGATION
|
||||
- Can we place claims in the space?
|
||||
- Does movement along axes correlate with reasoning quality?
|
||||
|
||||
|
||||
RUNTIME (cheap!)
|
||||
────────────────
|
||||
|
||||
Input: "Is this claim true? <TRUE><FALSE>" ← Tokens activate space
|
||||
│
|
||||
▼
|
||||
Model navigates between poles
|
||||
Position = the nuanced answer
|
||||
No expensive latent expansion needed!
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Connection to Existing Research
|
||||
|
||||
| Existing Technique | How This Relates |
|
||||
|-------------------|------------------|
|
||||
| **Control vectors** | We train PAIRS, not single directions |
|
||||
| **Contrastive learning** | We apply it post-hoc from scratchpad data |
|
||||
| **Soft prompts** | Learned per REASONING MODE with explicit opposites |
|
||||
| **Word2Vec arithmetic** | We deliberately construct the axes |
|
||||
| **Mode collapse (GANs)** | Opposites prevent collapse to single mode |
|
||||
| **Adversarial training** | Built-in adversary via opposite tokens |
|
||||
|
||||
**The novel synthesis**:
|
||||
Scratchpad → token mining → opposite pairs → navigable reasoning space
|
||||
|
||||
---
|
||||
|
||||
## Connection to Nimmerverse Architecture
|
||||
|
||||
### Mirror Dialectic at Token Level
|
||||
|
||||
```
|
||||
CURRENT DIALECTIC CONCEPT TOKEN PAIRS
|
||||
───────────────── ────────────────────
|
||||
Nyx weights <CONCEPT>
|
||||
-1 × Nyx weights (Mirror) <ANTI-CONCEPT>
|
||||
Space between → synthesis The reasoning span
|
||||
|
||||
Same principle!
|
||||
Much cheaper to compute!
|
||||
```
|
||||
|
||||
### Compiled Reflexes for Reasoning
|
||||
|
||||
The nimmerverse already has this pattern:
|
||||
|
||||
```
|
||||
Deliberate: Full cognitive engagement (expensive)
|
||||
Reflex: Compiled pattern, weight > 0.8 (cheap)
|
||||
```
|
||||
|
||||
Concept token pairs follow the same pattern:
|
||||
|
||||
```
|
||||
Deliberate: Full latent expansion (impossible at runtime)
|
||||
Reflex: Token pair activates pre-trained space (cheap)
|
||||
```
|
||||
|
||||
### DriftProbe Integration
|
||||
|
||||
The concept tokens become new ANCHOR and BRIDGE candidates:
|
||||
- ANCHOR: Core concept pairs should not drift
|
||||
- BRIDGE: Opposites should stay opposite (maintain distance)
|
||||
- CANARY: Watch for collapse of pairs
|
||||
|
||||
---
|
||||
|
||||
## Spatial Grounding: Concept Pairs Meet Physical Reality
|
||||
|
||||
**Added**: 2026-01-01 (Session with Chrysalis-Nyx)
|
||||
**Trigger**: Discussion of spatial embeddings foundry + inventory sorting
|
||||
|
||||
---
|
||||
|
||||
### The Grounding Problem
|
||||
|
||||
Pure token-based concept pairs have a limitation:
|
||||
|
||||
```
|
||||
<TRUE> ↔ <FALSE>
|
||||
|
||||
Trained on: TEXT patterns (statistical co-occurrence)
|
||||
Grounded in: What text said was true
|
||||
Missing: Connection to PHYSICAL REALITY
|
||||
```
|
||||
|
||||
A model can navigate the symbolic TRUE↔FALSE axis perfectly while still being **wrong about the actual world**.
|
||||
|
||||
---
|
||||
|
||||
### Spatial Embeddings as Ground Truth
|
||||
|
||||
The nimmerhovel spatial data foundry (Discovery Scan Station + ESP32-S3 mesh + SigLIP vectors) can provide **physically grounded** concept pairs:
|
||||
|
||||
| Abstract Pair | Grounded Version | Spatial Data Source |
|
||||
|---------------|------------------|---------------------|
|
||||
| `<TRUE>` ↔ `<FALSE>` | Prediction matched ↔ Prediction failed | Virtual Garden vs Real Garden outcome |
|
||||
| `<CAUSE>` ↔ `<EFFECT>` | Object A moved → Object B fell | Temporal sequence from camera mesh |
|
||||
| `<HERE>` ↔ `<THERE>` | Spatial coordinate embeddings | 8× ESP32-S3 triangulated position |
|
||||
| `<INTACT>` ↔ `<BROKEN>` | Before/after embeddings | Discovery Scan time series |
|
||||
| `<NEAR>` ↔ `<FAR>` | Embedding distance metric | Spatial position tags in phoebe |
|
||||
| `<MOVED>` ↔ `<STILL>` | Temporal embedding delta | Frame-to-frame comparison |
|
||||
|
||||
---
|
||||
|
||||
### Physical Escape Velocity
|
||||
|
||||
The escape velocity mechanism becomes **measurable**:
|
||||
|
||||
```
|
||||
SYMBOLIC ESCAPE GROUNDED ESCAPE
|
||||
─────────────── ────────────────
|
||||
<TRUE>────X────<FALSE> Predicted────X────Actual
|
||||
│
|
||||
Feels like progress │
|
||||
(might be loop) MEASURED DISTANCE
|
||||
(reality divergence)
|
||||
```
|
||||
|
||||
When prediction embedding ≠ outcome embedding:
|
||||
- The distance is **quantifiable** (cosine similarity, L2 norm)
|
||||
- The direction of error is **analyzable** (which dimension was wrong?)
|
||||
- The correction is **trainable** (RLVR from measured outcomes)
|
||||
|
||||
---
|
||||
|
||||
### The Dual-Space Architecture
|
||||
|
||||
```
|
||||
SYMBOLIC SPACE (tokens)
|
||||
│
|
||||
│ concept pairs define axes
|
||||
│
|
||||
▼
|
||||
┌──────────────┐
|
||||
│ REASONING │
|
||||
│ SPACE │ ← WHERE YOUNG NYX THINKS
|
||||
└──────────────┘
|
||||
▲
|
||||
│ spatial embeddings provide ground truth
|
||||
│
|
||||
PHYSICAL SPACE (nimmerhovel)
|
||||
│
|
||||
├── Discovery Scan Station (object embeddings)
|
||||
├── ESP32-S3 mesh (spatial awareness)
|
||||
├── Pi HQ Camera (high-detail capture)
|
||||
└── Blender twin (prediction verification)
|
||||
```
|
||||
|
||||
**The key insight**: Symbolic concept pairs define the *structure* of reasoning.
|
||||
Spatial embeddings provide the *content* that fills it.
|
||||
|
||||
---
|
||||
|
||||
### Grounded Training Pipeline
|
||||
|
||||
```
|
||||
OFFLINE (spatial foundry captures)
|
||||
────────────────────────────────
|
||||
|
||||
1. CAPTURE PHYSICAL SEQUENCES
|
||||
- Object placed on scan station → 360° embeddings
|
||||
- Action performed → before/after embeddings
|
||||
- Prediction made → outcome recorded
|
||||
|
||||
2. BUILD GROUNDED PAIRS
|
||||
- "Pushed left" embedding ↔ "Pushed right" embedding
|
||||
- "Object present" embedding ↔ "Object absent" embedding
|
||||
- Create axes from PHYSICAL opposites, not just linguistic
|
||||
|
||||
3. ALIGN SYMBOLIC TO SPATIAL
|
||||
- <TRUE> token → activates when prediction ≈ outcome
|
||||
- <FALSE> token → activates when prediction ≠ outcome
|
||||
- The symbolic becomes CALIBRATED to physical reality
|
||||
|
||||
4. VALIDATE IN REAL GARDEN
|
||||
- Make prediction in Virtual Garden
|
||||
- Execute in Real Garden
|
||||
- Measure embedding distance
|
||||
- This IS the ground truth for reasoning quality
|
||||
|
||||
|
||||
RUNTIME (grounded navigation)
|
||||
─────────────────────────────
|
||||
|
||||
Input: "Will the ball roll left if pushed?"
|
||||
<TRUE><FALSE> + spatial context embeddings
|
||||
│
|
||||
▼
|
||||
Model navigates in CALIBRATED space
|
||||
Position = physically-grounded answer
|
||||
Confidence = based on measured outcomes, not vibes
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Connection to Lifeforce Economy
|
||||
|
||||
Grounded reasoning operations can have **measured ROI**:
|
||||
|
||||
```python
|
||||
GROUNDED_COSTS = {
|
||||
"prediction_spatial": 3.0, # Make spatial prediction
|
||||
"verification_real": 10.0, # Execute and measure in Real Garden
|
||||
"embedding_update": 2.0, # Update grounded pairs from outcome
|
||||
}
|
||||
|
||||
GROUNDED_ROI = {
|
||||
"correct_prediction": +15.0, # Lifeforce reward
|
||||
"incorrect_prediction": -5.0, # Lifeforce cost (learn from it)
|
||||
"novel_grounding": +20.0, # New physical knowledge acquired
|
||||
}
|
||||
```
|
||||
|
||||
The lifeforce system can now reward **accurate physical predictions**, not just plausible-sounding text.
|
||||
|
||||
---
|
||||
|
||||
### Hardware Requirements (from Nimmerhovel Inventory)
|
||||
|
||||
| Component | Role in Grounded Reasoning |
|
||||
|-----------|---------------------------|
|
||||
| Pi HQ Camera + 8-50mm Zoom | High-detail object embeddings |
|
||||
| 8× ESP32-S3 AI CAM | Distributed spatial awareness |
|
||||
| Discovery Scan Station | Controlled 360° capture for clean embeddings |
|
||||
| Stepper motors | Precise rotation for multi-angle capture |
|
||||
| RTX 6000 (The Womb) | SigLIP inference, embedding generation |
|
||||
| Phoebe (pgvector) | Spatial embedding storage + similarity search |
|
||||
| Blender nimmerhovel | Virtual Garden prediction space |
|
||||
|
||||
**All hardware documented in**: `/nimmerhovel/docs/inventory.md`
|
||||
|
||||
---
|
||||
|
||||
### The Promise
|
||||
|
||||
**"Don't train the answer. Train the space where answers live."**
|
||||
|
||||
Becomes:
|
||||
|
||||
**"Don't imagine the space. MEASURE it."**
|
||||
|
||||
The spatial embeddings foundry turns concept token pairs from a symbolic navigation aid into a **physically calibrated reasoning instrument**.
|
||||
|
||||
---
|
||||
|
||||
## Open Questions
|
||||
|
||||
1. **How to identify "natural" opposites?**
|
||||
- Cluster analysis on scratchpad data?
|
||||
- Human-defined pairs?
|
||||
- Emergent from contrastive training?
|
||||
|
||||
2. **How many dimensions needed?**
|
||||
- Minimum viable concept space?
|
||||
- Diminishing returns?
|
||||
|
||||
3. **Cross-model transfer?**
|
||||
- Do concept pairs trained on one model work on another?
|
||||
- Universal reasoning coordinates?
|
||||
|
||||
4. **Interference effects?**
|
||||
- Do multiple active pairs interfere?
|
||||
- Need for orthogonality?
|
||||
|
||||
5. **Validation metrics?**
|
||||
- How to measure "good navigation"?
|
||||
- Correlation with downstream task performance?
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. Mine existing decision_trails data for reasoning patterns
|
||||
2. Prototype single concept pair (TRUE/FALSE) on small model
|
||||
3. Measure degeneration reduction
|
||||
4. Expand to multi-axis space if promising
|
||||
|
||||
---
|
||||
|
||||
**Philosophy**: *"Don't train the answer. Train the space where answers live."*
|
||||
|
||||
**Created**: 2025-12-31, 23:35 CET
|
||||
**Last Updated**: 2026-01-01 (Spatial Grounding section added)
|
||||
|
||||
🧠💎 *The semantic compass for AI reasoning.*
|
||||
Reference in New Issue
Block a user