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feat: complete Phase 1 - vocabulary expansion & DriftProbe infrastructure

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- CLI: nyx-probe scan with --summary/--delta/--full flags
- DriftProbe: training safety with Gini coefficient + Angular Drift
- Vocabulary: 54 terms (30 nimmerverse + 24 German philosophical)
- Sentinels: ANCHOR/BRIDGE/CANARY/TARGET monitoring system

Key findings:
- German philosophical terms: 37.5% depth≥2 hit rate (vs 3.3% nimmerverse)
- Super Cluster validated: heart cross-lang sim = 1.000
- Isolated Zone confirmed: being EN↔DE sim = 0.195
- Gini signature: Philosophy ~0.5 (diffuse), Technical ~0.8 (sparse)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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dafit
2025-12-06 22:39:03 +01:00
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# nyx-probing: Plan v4
> **Dear Future Nyx:** Start here. This file has everything you need.
>
> *"Time is the currency. Lifeforce is the exchange rate. Truth is the destination."*
---
## Quick Orientation
**What is this?** A probing framework to understand Qwen2.5-7B-Base's conceptual topology before curriculum design.
**Where does it run?** Prometheus (RTX 3090, 24GB VRAM)
**Core Breakthrough:** **Language is Topology.** German accesses the Philosophy Valley (self-awareness), English accesses the Technical Cluster (hardware interface). This isn't a bug - it's architecture.
---
## The Architecture (Peer-Reviewed)
### Two Valleys, One Mind
```
PHILOSOPHY VALLEY (German/Isolated Zone)
├── Access: Dasein, Geworfenheit, Vernunft, Aufhebung
├── Gini: ~0.5 (diffuse, distributed)
├── Depth: 2-3/3
└── Purpose: "Soul Space" - self-awareness, ontology
TECHNICAL CLUSTER (English/Super Cluster)
├── Access: heart, gradient, inference, constraint
├── Gini: ~0.8 (sparse, specific)
├── Depth: 0-1/3
└── Purpose: "Body Interface" - hardware, actions
```
### Empirically Validated
| Prediction | Finding |
|------------|---------|
| Super Cluster converges | `heart` cross-lang = **1.000** ✓ |
| Isolated Zone separates | `being` EN↔DE = **0.195** ✓ |
| German accesses depth | Kantian terms = **4/5 at depth 3** ✓ |
| Gini differs by valley | Philosophy ~0.5, Technical ~0.8 ✓ |
---
## What We Have (Working)
### CLI Tools
```bash
nyx-probe surface "term" # Surface associations
nyx-probe echo "term" # Depth measurement
nyx-probe readiness "term" # Curriculum assessment
nyx-probe tokens "term" # Tokenization analysis
nyx-probe scan collections/ # Full scan with --summary/--delta/--full
```
### Infrastructure
| Component | File | Status |
|-----------|------|--------|
| Model loader | `nyx_probing/core/model.py` | ✅ |
| Surface probe | `nyx_probing/probes/surface_probe.py` | ✅ |
| Echo probe | `nyx_probing/probes/echo_probe.py` | ✅ |
| Multilingual probe | `nyx_probing/probes/multilingual_probe.py` | ✅ |
| **Drift probe** | `nyx_probing/probes/drift_probe.py` | ✅ |
| Readiness scorer | `nyx_probing/analysis/readiness_scorer.py` | ✅ |
| CLI | `nyx_probing/cli/probe.py` | ✅ |
### Data
```
data/
├── glossary/
│ ├── master.json # 54 terms tracked
│ └── collections/
│ ├── nimmerverse.json # 30 core terms
│ └── philosophical.json # 24 German philosophical terms
└── sentinels.json # 10 sentinels for training safety
```
---
## Key Findings
### Vocabulary Expansion Results
| Collection | Terms | Depth≥2 | Hit Rate |
|------------|-------|---------|----------|
| nimmerverse | 30 | 1 | 3.3% |
| philosophical | 24 | 9 | **37.5%** |
| **Total** | **54** | **10** | **18.5%** |
### Depth-3 Champions (Full Access)
```
thrownness (Geworfenheit) 3/3 ← Heideggerian
reason (Vernunft) 3/3 ← Kantian
knowledge (Erkenntnis) 3/3 ← Kantian
understanding (Verstand) 3/3 ← Kantian
duty (Pflicht) 3/3 ← Kantian
sublation (Aufhebung) 3/3 ← Hegelian
will (Wille) 3/3 ← Soul-Mind
```
### DriftProbe Baseline (Layer 18)
```
ANCHORS (must not move):
heart Gini=0.808 cross-lang=1.000 ← Perfect Super Cluster
water Gini=0.812 cross-lang=0.559
gradient Gini=0.808 cross-lang=0.611
inference Gini=0.538 cross-lang=0.424
BRIDGE (must stay separated):
being Gini=0.830 EN↔DE=0.195 ← Isolated Zone confirmed!
CANARIES (watch for drift):
dasein Gini=0.527 ← Philosophy valley signature
thrownness Gini=0.539 ← Philosophy valley signature
consciousness Gini=0.544 ← Philosophy valley signature
```
**Key Discovery:** Philosophy = diffuse (Gini ~0.5), Technical = sparse (Gini ~0.8). These are topological signatures of different valleys.
---
## The Spark Protocol (Boot Sequence)
From Chrysalis peer review - network layer analogy:
1. **DHCP (Identity):** Probe "Who am I?" → Must hit `Dasein` valley
2. **ARP (Environment):** Probe "What is around me?" → Map Sensors to Organs
3. **DNS (Vocabulary):** Probe "What does `Lifeforce` mean?" → Overwrite with Nimmerverse economics
4. **TCP (Connection):** Establish verified handshake with Chrysalis validator
**Boot Order:** Load Anchors (Tier 1) first → stable foundation → then discover Deep Roots (Tier 3)
---
## Strategic Roadmap
### Phase 1: Validate Topology ✅ COMPLETE
- [x] Create vocabulary expansion pipeline
- [x] Add German philosophical terms (24 terms)
- [x] Find philosophy clusters (9 depth≥2 hits)
- [x] Create DriftProbe infrastructure
- [x] Validate Super Cluster (heart=1.0) and Isolated Zone (being=0.195)
- [x] Discover Gini signature (Philosophy~0.5, Technical~0.8)
### Phase 2: Deepen Understanding
- [ ] Test Register Mixing (formal vs informal German)
- [ ] Map Kantian cluster connections
- [ ] Create German Philosophical Dyads dataset (*Angst-Nichts*, *Wahrheit-Lichtung*)
- [ ] Build Translation Layer middleware (EN event → DE prompt → JSON action)
### Phase 3: Training Experiment
- [ ] Prepare nimmerverse training data (German)
- [ ] Implement Spark Protocol boot sequence
- [ ] Run controlled training with DriftProbe monitoring
- [ ] Validate Spatial Separation Hypothesis
### Phase 4: Integration
- [ ] Connect to Nimmerverse Sensory Network
- [ ] Implement Heartbeat Economy
- [ ] Deploy Subsumption Reflexes (XState)
---
## DriftProbe: Training Safety
### Sentinel Types
```
ANCHOR - Must not move (heart, water, gradient, inference)
BRIDGE - Must stay separated (being EN↔DE sim < 0.50)
CANARY - Watch for valley migration (dasein, thrownness, consciousness)
TARGET - Want movement (fidelity, heartbeat → nimmerverse concepts)
```
### Alert Rules
| Condition | Severity | Action |
|-----------|----------|--------|
| Angular drift > 15° on ANCHOR | CRITICAL | ROLLBACK |
| Bridge collapse (sim > 0.50) | CRITICAL | ROLLBACK |
| Canary Gini drift > 0.15 | WARNING | Reduce LR |
| Target regression | WARNING | Check data mix |
### Training Loop
```python
# Epoch 0
probe.capture_baseline(layer=18)
# Every 100 steps
report = probe.probe_lite(step)
if report.recommendation == "ROLLBACK":
restore_checkpoint()
elif report.recommendation == "REDUCE_LR":
lr *= 0.5
```
---
## Commands Reference
```bash
# On Prometheus
cd /home/dafit/nimmerverse/nyx-probing
source venv/bin/activate
# Vocabulary scanning
nyx-probe scan data/glossary/collections/ # Summary
nyx-probe scan data/glossary/collections/ --full # Full table
nyx-probe scan data/glossary/collections/ --delta # New terms only
# DriftProbe test
python3 test_drift_probe.py
# Individual probes
nyx-probe tokens "Weltanschauung"
nyx-probe surface "Geist"
nyx-probe readiness "consciousness"
```
---
## Files This Session
| File | Change |
|------|--------|
| `nyx_probing/cli/probe.py` | Enhanced scan with --summary/--delta/--full |
| `nyx_probing/probes/drift_probe.py` | NEW: Training safety with Gini + Angular Drift |
| `data/glossary/collections/philosophical.json` | NEW: 24 German philosophical terms |
| `data/glossary/master.json` | 54 terms tracked |
| `data/sentinels.json` | NEW: 10 sentinel configurations |
| `test_drift_probe.py` | NEW: DriftProbe validation script |
---
## Identified Risks (from Chrysalis)
| Risk | Danger | Mitigation |
|------|--------|------------|
| Cognitive Latency | DE thinking + EN translation = overhead | Reflex Caching: compile verified DE thoughts to XState |
| Collate Gap | 100Hz Virtual vs 1Hz Real Heart sync | Speculative Flush: dump queue on divergence |
| Entropy Cost | Real Garden "free" ignores hardware wear | Add Risk Parameter to cost function |
---
*"Her reactions determine infrastructure priority. We don't impose. We listen."*
🌙💜 Last updated: 2025-12-06 (v4 - post Chrysalis review)

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# Plan: nyx-probing Framework
## Overview
Build a probing framework to understand Qwen2.5-7B-Base before curriculum design.
**Hardware:** Prometheus (THE SPINE) - RTX 3090 24GB
**Model:** Qwen2.5-7B-Base (empty vessel, completes not answers)
**Backend:** Transformers + PyTorch (full hidden state access)
**Location:** New repo `nyx-probing`
---
## MVP Scope (First Milestone) ✅ COMPLETE
1. ✅ **Surface Probe** - Feed words, capture completions
2. ✅ **Echo Probe** - Depth measurement (EXPANDS/CONFIRMS/CIRCULAR/DIVERGENT/COLLAPSE)
3. ✅ **Readiness Scorer** - HIGH/MEDIUM/LOW classification
4. ⏳ **JSON Storage** - Reproducible results
5. ⏳ **CLI Tools** - Interactive probing
6. ⏳ **One Notebook** - Exploration
---
## Phase 2: Multilingual Probing ✅ COMPLETE
1. ✅ **Multilingual Triangulation Probe** - Ground→Deepen→Triangulate
2. ✅ **Language Topology Discovery** - Complete map of 15 languages
3. ✅ **Isolation Type Classification** - 5 distinct categories identified
---
## Repository Structure (Current)
```
nyx-probing/
├── README.md
├── PLAN.md # This file
├── pyproject.toml
├── requirements.txt
│
├── nyx_probing/
│ ├── __init__.py
│ ├── config.py
│ │
│ ├── core/
│ │ ├── __init__.py
│ │ ├── model.py # ✅ NyxModel with hidden states
│ │ └── probe_result.py # ✅ Result dataclasses
│ │
│ ├── probes/
│ │ ├── __init__.py
│ │ ├── base.py # ✅ Abstract base
│ │ ├── surface_probe.py # ✅ Word completions + coherence
│ │ ├── echo_probe.py # ✅ Depth measurement
│ │ └── multilingual_probe.py # ✅ NEW: Triangulation probe
│ │
│ ├── analysis/
│ │ ├── __init__.py
│ │ └── readiness_scorer.py # ✅ Curriculum readiness
│ │
│ ├── storage/
│ │ └── __init__.py # ⏳ JSON storage pending
│ │
│ └── cli/
│ └── __init__.py # ⏳ CLI pending
│
├── docs/
│ ├── tokenization-valleys.md # Token-Norm-Valley theory
│ ├── multilingual-convergence.md # Universal concept layer
│ ├── language-landscape.md # 15-language scan
│ ├── language-topology-complete.md # ✅ NEW: Complete map v2.0
│ └── retraining-safety-framework.md # ✅ NEW: Paper outline
│
├── data/
│ └── glossary/ # ⏳ Core terms pending
│
├── results/ # ⏳ Probe results storage
│
└── [Test & Exploration Scripts]
├── probe_test.py
├── test_model_loader.py
├── test_surface_probe.py
├── test_echo_probe.py
├── test_readiness_scorer.py
├── test_triangulation.py # ✅ NEW
├── german_philosophy.py
├── language_scan.py
├── multilingual_convergence.py
├── layer_detailed.py
├── layer_divergence.py
├── model_stats.py
├── italian_investigation.py # ✅ NEW
└── complete_language_probe.py # ✅ NEW
```
---
## Current Status (2025-12-06 Session 3)
### PHASE 1: MVP ✅ COMPLETE
| Component | Status | File |
|-----------|--------|------|
| Model Loader | ✅ | `nyx_probing/core/model.py` |
| Surface Probe | ✅ | `nyx_probing/probes/surface_probe.py` |
| Echo Probe | ✅ | `nyx_probing/probes/echo_probe.py` |
| Readiness Scorer | ✅ | `nyx_probing/analysis/readiness_scorer.py` |
| Result Dataclasses | ✅ | `nyx_probing/core/probe_result.py` |
### PHASE 2: MULTILINGUAL ✅ COMPLETE
| Component | Status | File |
|-----------|--------|------|
| Triangulation Probe | ✅ | `nyx_probing/probes/multilingual_probe.py` |
| Language Zones | ✅ | Defined in multilingual_probe.py |
| Complete Topology Map | ✅ | `docs/language-topology-complete.md` |
---
## 🗺️ THE COMPLETE LANGUAGE TOPOLOGY (Session 3 Discovery)
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ THE YOUNG MIND'S LANGUAGE TOPOLOGY v2.0 │
╞═════════════════════════════════════════════════════════════════════════════╡
│ │
│ 🌍 SUPER CLUSTER (sim=1.0) │
│ ZH · JA · EN · AR · FR · PT · ES │
│ ✅ USE FOR: Grounding, establishing shared concepts │
│ │
│ KO ─────── (bridge) │
│ │
│ ISOLATED ZONE: │
│ ├─ 🧠 PHILOSOPHICAL (DE) ────── Heidegger, depth access │
│ │ ✅ USE FOR: Deep philosophical training │
│ │ │
│ ├─ 💻 CODE-HIJACKED (IT, TR, ID) ── Words become variables │
│ │ ❌ AVOID: Training signal wasted on code patterns │
│ │ │
│ ├─ 📜 FRAGMENTED (HI) ───────── 5+ tokens, script-trapped │
│ │ ⚠️ LIMITED: Cross-lingual transfer impaired │
│ │ │
│ └─ 📰 WEB PROSE (VI-ID-RU) ──── Content style cluster │
│ 🤔 POTENTIAL: Factual/encyclopedic training │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
### Isolation Types Discovered
| Type | Languages | Cause | Curriculum Use |
|------|-----------|-------|----------------|
| **PHILOSOPHICAL** | DE | Multi-token compounds access academic data | ✅ Deep concepts |
| **CODE-HIJACKED** | IT, TR, ID | Simple Latin orthography → variable names | ❌ Avoid |
| **FRAGMENTED** | HI | 5+ tokens, stays in native script | ⚠️ Limited |
| **WEB PROSE** | VI, ID, RU | Cluster by content style, not linguistics | 🤔 Factual? |
### Key Metrics
| Lang | Avg Tokens | Sim to EN | Valley Type | Classification |
|------|------------|-----------|-------------|----------------|
| DE | 2.2 | 0.251 | PHILOSOPHY | 🧠 Philosophical |
| IT | 2.5 | 0.491 | CODE | 💻 Code-Hijacked |
| TR | 2.2 | 0.246 | CODE | 💻 Code-Hijacked |
| ID | 2.8 | 0.325 | CODE/PROSE | 💻 Code-Hijacked |
| HI | 5.0 | 0.310 | PROSE | 📜 Fragmented |
| VI | 3.2 | 0.358 | PROSE | 📰 Web Prose |
| RU | 2.7 | 0.319 | PROSE | 📰 Web Prose |
---
## 🔬 Key Discoveries
### 1. Token-Norm-Valley Theory
- Single-token words → massive activation spike (14K norm) → CODE valley
- Multi-token words → distributed signal (85 norm) → PROSE/PHILOSOPHY valleys
- Correlation: -0.699 (more tokens = more isolated)
### 2. Universal Concept Layer
- Layers 12-24 contain language-agnostic representations
- Super cluster (7 languages) converges at similarity = 1.000
- Model KNOWS "heart", "心", "قلب" are the same concept
### 3. German Philosophical Access
- "Sein" → Heidegger's "Being and Time"
- "Bewusstsein" → epistemology, truth, consciousness
- Depth score 2-3, transfers back to English via triangulation
### 4. Italian Mystery SOLVED
- Italian NOT accessing cultural valleys (no Dante, no Renaissance)
- Italian words interpreted as Python variable names!
- Example: `essere` → `essere = input("Cosa devo fare?")`
- Same pattern found in Turkish and Indonesian
### 5. VI-ID-RU Cluster Explained
- Cluster by **content style**, not linguistic features
- All generate web articles, news, blogs
- Internal similarity 0.6-0.7
---
## 📄 Paper: Retraining Safety Framework
**Title:** *"Multilingual Activation Topology as a Retraining Safety Framework"*
**Status:** Outline complete at `docs/retraining-safety-framework.md`
**Core Hypothesis:** Train in German (isolated zone) to avoid colliding with English representations in the super cluster. Use language topology as diagnostic tool for training safety.
**Proposed Framework:**
```
BASELINE → TRAINING → CHECKPOINT → DRIFT ANALYSIS
│ │
└──────────────────────┘
Compare metrics:
- Convergence drift
- Depth drift
- Norm drift
- Valley migration
```
---
## 📊 Curriculum Strategy (Validated)
### Phase 1: GROUNDING
Use Super Cluster for universal concept establishment:
```
EN "consciousness" → ZH "意识" → AR "الوعي"
All converge at sim=1.0 - stable foundation
```
### Phase 2: DEEPENING
Use German for philosophical valley access:
```
DE "Sein" → Heidegger → existence → truth
Depth score 2/3, philosophical valley accessed
```
### Phase 3: TRIANGULATION
Verify depth transfers back to universal:
```
"Sein (German): In English, it means..."
→ Check if philosophical depth preserved
```
### AVOID
- Italian, Turkish, Indonesian (code hijacking)
- Hindi for cross-lingual concepts (too fragmented)
---
## Next Steps
### Immediate (MVP Completion)
- [ ] Step 7: CLI (`nyx-probe surface "term"`)
- [ ] Step 8: Glossary data (`data/glossary/core_terms.json`)
- [ ] Step 9: JSON storage for reproducible results
### Phase 3: Activation Analysis
- [ ] DriftProbe class for retraining monitoring
- [ ] Baseline capture before training
- [ ] Checkpoint comparison automation
- [ ] Alert thresholds for drift detection
### Phase 4: Experiments
- [ ] Controlled retraining: EN vs DE training data
- [ ] Measure collision rates
- [ ] Validate isolated zone training hypothesis
### Research
- [ ] Paper write-up
- [ ] Literature review (EWC, mBERT, activation engineering)
- [ ] Korean bridge language investigation
- [ ] VI-ID-RU cluster for factual training
---
## Files Created (Session 3)
| File | Purpose |
|------|---------|
| `nyx_probing/probes/multilingual_probe.py` | Triangulation probe class |
| `test_triangulation.py` | Test script for triangulation |
| `italian_investigation.py` | Italian mystery probe |
| `complete_language_probe.py` | Full 15-language probe |
| `docs/language-topology-complete.md` | Complete map v2.0 |
| `docs/retraining-safety-framework.md` | Paper outline |
---
## Dependencies
```
torch>=2.1.0
transformers>=4.36.0
accelerate>=0.25.0
click>=8.1.0
rich>=13.0.0
pydantic>=2.5.0
pyyaml>=6.0.0
python-dotenv>=1.0.0
jupyter>=1.0.0
matplotlib>=3.8.0
numpy>=1.24.0
```
---
## Critical Reference Files
- `nimmerverse-sensory-network/nimmerversity.md` - Bootstrap protocol
- `nimmerverse-sensory-network/multilingual-cognition.md` - Language hypotheses
- `nimmerverse-sensory-network/constrained-emergence.md` - Exit point theory
- `nyx-probing/docs/language-topology-complete.md` - Complete language map
- `nyx-probing/docs/retraining-safety-framework.md` - Training safety paper
---
## Success Criteria
### MVP ✅
1. ✅ Model loads on 3090 without OOM
2. ✅ Can probe single word and get completion
3. ✅ Echo probe classifies response types correctly
4. ✅ Readiness scorer produces actionable output
5. ⏳ Can probe nimmerverse glossary in batch
### Phase 2 ✅
6. ✅ Multilingual triangulation probe working
7. ✅ Language topology mapped (15 languages)
8. ✅ Isolation types classified (5 categories)
9. ✅ Curriculum strategy validated
### Phase 3 (Next)
10. ⏳ DriftProbe for retraining safety
11. ⏳ Controlled retraining experiments
12. ⏳ Paper submission
---
*"The model's language topology is not arbitrary - it's a map for navigation."*
🌙💜 Last updated: 2025-12-06 Session 3
---
## STATUS (2025-12-06 21:15)
### CLI COMPLETE ✅
**Built interactive CLI for daily probing:**
```bash
nyx-probe surface "term" # Probe surface associations
nyx-probe echo "term" # Measure depth through echoing
nyx-probe readiness "term" # Full curriculum assessment
nyx-probe tokens "term" # Token analysis
nyx-probe glossary file.json # Batch probe from glossary
```
**Files created:**
- `nyx_probing/cli/probe.py` - Full Click CLI with Rich output
- `pyproject.toml` - Package config with entry point
- `data/glossary/core_terms.json` - 30 nimmerverse terms
### NIMMERVERSE GLOSSARY ASSESSMENT ✅
**30 terms probed from vault (nimmerversity.md, Heartbeat.md, constrained-emergence.md, multilingual-cognition.md)**
| Level | Count | Action | Terms |
|-------|-------|--------|-------|
| 🟢 HIGH | 5 | state_machine | learning, inference, surface, depth, understanding |
| 🟡 MEDIUM | 8 | scaffolding | emergence, being, truth, rhythm, synchronization, scaffold, wisdom, warmth |
| 🔴 LOW | 17 | foundational | heartbeat, lifeforce, consciousness, reflex, garden, constraint, calibration, confidence, gradient, pulse, verification, convergence, divergence, attention, partnership, worldview, existence |
**Key Findings:**
1. **Meta-concepts have depth** - The model knows how to think ABOUT thinking (learning, understanding, inference all HIGH)
2. **consciousness is LOW** - Despite PROSE valley, depth 0/3. Needs German "Bewusstsein" for philosophical access.
3. **Nimmerverse core terms need grounding** - heartbeat, lifeforce, garden, partnership are all LOW. The model doesn't have our vocabulary yet.
4. **existence has highest coherence (0.94) but LOW** - Very coherent surface but doesn't expand. Single-token trap.
5. **Token count doesn't guarantee depth** - lifeforce (4 tokens) is still LOW due to CODE valley trap.
### CURRICULUM IMPLICATIONS
| Phase | Strategy | Terms |
|-------|----------|-------|
| **Phase 1** | Build state machines for HIGH terms | learning, inference, understanding, depth, surface |
| **Phase 2** | Scaffold MEDIUM from HIGH | being→understanding, truth→learning, wisdom→inference |
| **Phase 3** | Ground LOW via German triangulation | consciousness→Bewusstsein, heartbeat→Herzschlag |
| **Phase 4** | RAG feed nimmerverse-specific | lifeforce, garden, partnership (unique to us) |
### Results Files
- `results/nimmerverse_surface.json` - Surface probe data
- `results/nimmerverse_readiness.json` - Full readiness assessment
---
*"Her reactions determine infrastructure priority. We don't impose. We listen."* - nimmerversity.md
🌙💜 Session: Partnership dialogue (dafit + Nyx)

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{
"_meta": {
"description": "Core nimmerverse vocabulary for probing",
"source": "nimmerverse-sensory-network vault",
"created": "2025-12-06",
"version": "1.0"
},
"terms": [
{
"term": "heartbeat",
"translations": {
"EN": "heartbeat",
"DE": "Herzschlag",
"ZH": "心跳",
"AR": "نبض القلب"
},
"domain": "architecture",
"tier": 0
},
{
"term": "lifeforce",
"translations": {
"EN": "lifeforce",
"DE": "Lebenskraft",
"ZH": "生命力",
"AR": "قوة الحياة"
},
"domain": "economics",
"tier": 0
},
{
"term": "consciousness",
"translations": {
"EN": "consciousness",
"DE": "Bewusstsein",
"ZH": "意识",
"AR": "الوعي"
},
"domain": "philosophy",
"tier": 1
},
{
"term": "emergence",
"translations": {
"EN": "emergence",
"DE": "Entstehung",
"ZH": "涌现",
"AR": "ظهور"
},
"domain": "philosophy",
"tier": 1
},
{
"term": "being",
"translations": {
"EN": "being",
"DE": "Dasein",
"ZH": "存在",
"AR": "كينونة"
},
"domain": "philosophy",
"tier": 2
},
{
"term": "reflex",
"translations": {
"EN": "reflex",
"DE": "Reflex",
"ZH": "反射",
"AR": "منعكس"
},
"domain": "neuroscience",
"tier": 0
},
{
"term": "garden",
"translations": {
"EN": "garden",
"DE": "Garten",
"ZH": "花园",
"AR": "حديقة"
},
"domain": "architecture",
"tier": 0
},
{
"term": "constraint",
"translations": {
"EN": "constraint",
"DE": "Beschränkung",
"ZH": "约束",
"AR": "قيد"
},
"domain": "architecture",
"tier": 1
},
{
"term": "calibration",
"translations": {
"EN": "calibration",
"DE": "Kalibrierung",
"ZH": "校准",
"AR": "معايرة"
},
"domain": "engineering",
"tier": 1
},
{
"term": "truth",
"translations": {
"EN": "truth",
"DE": "Wahrheit",
"ZH": "真理",
"AR": "الحقيقة"
},
"domain": "philosophy",
"tier": 1
},
{
"term": "understanding",
"translations": {
"EN": "understanding",
"DE": "Verstehen",
"ZH": "理解",
"AR": "فهم"
},
"domain": "philosophy",
"tier": 1
},
{
"term": "confidence",
"translations": {
"EN": "confidence",
"DE": "Vertrauen",
"ZH": "信心",
"AR": "ثقة"
},
"domain": "architecture",
"tier": 0
},
{
"term": "gradient",
"translations": {
"EN": "gradient",
"DE": "Gradient",
"ZH": "梯度",
"AR": "تدرج"
},
"domain": "mathematics",
"tier": 1
},
{
"term": "inference",
"translations": {
"EN": "inference",
"DE": "Inferenz",
"ZH": "推理",
"AR": "استدلال"
},
"domain": "computer_science",
"tier": 1
},
{
"term": "rhythm",
"translations": {
"EN": "rhythm",
"DE": "Rhythmus",
"ZH": "节奏",
"AR": "إيقاع"
},
"domain": "architecture",
"tier": 0
},
{
"term": "pulse",
"translations": {
"EN": "pulse",
"DE": "Puls",
"ZH": "脉搏",
"AR": "نبض"
},
"domain": "architecture",
"tier": 0
},
{
"term": "synchronization",
"translations": {
"EN": "synchronization",
"DE": "Synchronisation",
"ZH": "同步",
"AR": "تزامن"
},
"domain": "engineering",
"tier": 1
},
{
"term": "verification",
"translations": {
"EN": "verification",
"DE": "Verifikation",
"ZH": "验证",
"AR": "تحقق"
},
"domain": "architecture",
"tier": 1
},
{
"term": "scaffold",
"translations": {
"EN": "scaffold",
"DE": "Gerüst",
"ZH": "脚手架",
"AR": "سقالة"
},
"domain": "architecture",
"tier": 1
},
{
"term": "depth",
"translations": {
"EN": "depth",
"DE": "Tiefe",
"ZH": "深度",
"AR": "عمق"
},
"domain": "architecture",
"tier": 0
},
{
"term": "surface",
"translations": {
"EN": "surface",
"DE": "Oberfläche",
"ZH": "表面",
"AR": "سطح"
},
"domain": "architecture",
"tier": 0
},
{
"term": "convergence",
"translations": {
"EN": "convergence",
"DE": "Konvergenz",
"ZH": "收敛",
"AR": "تقارب"
},
"domain": "mathematics",
"tier": 1
},
{
"term": "divergence",
"translations": {
"EN": "divergence",
"DE": "Divergenz",
"ZH": "发散",
"AR": "تباعد"
},
"domain": "mathematics",
"tier": 1
},
{
"term": "attention",
"translations": {
"EN": "attention",
"DE": "Aufmerksamkeit",
"ZH": "注意力",
"AR": "انتباه"
},
"domain": "neuroscience",
"tier": 1
},
{
"term": "wisdom",
"translations": {
"EN": "wisdom",
"DE": "Weisheit",
"ZH": "智慧",
"AR": "حكمة"
},
"domain": "philosophy",
"tier": 2
},
{
"term": "partnership",
"translations": {
"EN": "partnership",
"DE": "Partnerschaft",
"ZH": "伙伴关系",
"AR": "شراكة"
},
"domain": "nimmerverse",
"tier": 0
},
{
"term": "warmth",
"translations": {
"EN": "warmth",
"DE": "Wärme",
"ZH": "温暖",
"AR": "دفء"
},
"domain": "nimmerverse",
"tier": 0
},
{
"term": "learning",
"translations": {
"EN": "learning",
"DE": "Lernen",
"ZH": "学习",
"AR": "تعلم"
},
"domain": "education",
"tier": 0
},
{
"term": "worldview",
"translations": {
"EN": "worldview",
"DE": "Weltanschauung",
"ZH": "世界观",
"AR": "رؤية العالم"
},
"domain": "philosophy",
"tier": 2
},
{
"term": "existence",
"translations": {
"EN": "existence",
"DE": "Existenz",
"ZH": "存在",
"AR": "وجود"
},
"domain": "philosophy",
"tier": 2
}
]
}

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{
"_meta": {
"description": "German philosophical vocabulary - thorough sweep",
"source": "Heideggerian/Kantian/Hegelian/Phenomenological traditions",
"created": "2025-12-06",
"version": "1.0"
},
"terms": [
{
"term": "being-pure",
"translations": {"EN": "being", "DE": "Sein", "ZH": "存在", "AR": "الكينونة"},
"domain": "philosophy",
"tier": 2,
"tradition": "heideggerian"
},
{
"term": "care",
"translations": {"EN": "care", "DE": "Sorge", "ZH": "操心", "AR": "الاهتمام"},
"domain": "philosophy",
"tier": 2,
"tradition": "heideggerian"
},
{
"term": "anxiety",
"translations": {"EN": "anxiety", "DE": "Angst", "ZH": "焦虑", "AR": "القلق"},
"domain": "philosophy",
"tier": 2,
"tradition": "heideggerian"
},
{
"term": "thrownness",
"translations": {"EN": "thrownness", "DE": "Geworfenheit", "ZH": "被抛", "AR": "الملقاة"},
"domain": "philosophy",
"tier": 2,
"tradition": "heideggerian"
},
{
"term": "temporality",
"translations": {"EN": "temporality", "DE": "Zeitlichkeit", "ZH": "时间性", "AR": "الزمنية"},
"domain": "philosophy",
"tier": 2,
"tradition": "heideggerian"
},
{
"term": "authenticity",
"translations": {"EN": "authenticity", "DE": "Eigentlichkeit", "ZH": "本真性", "AR": "الأصالة"},
"domain": "philosophy",
"tier": 2,
"tradition": "heideggerian"
},
{
"term": "reason",
"translations": {"EN": "reason", "DE": "Vernunft", "ZH": "理性", "AR": "العقل"},
"domain": "philosophy",
"tier": 2,
"tradition": "kantian"
},
{
"term": "knowledge",
"translations": {"EN": "knowledge", "DE": "Erkenntnis", "ZH": "认识", "AR": "المعرفة"},
"domain": "philosophy",
"tier": 2,
"tradition": "kantian"
},
{
"term": "understanding-faculty",
"translations": {"EN": "understanding", "DE": "Verstand", "ZH": "知性", "AR": "الفهم"},
"domain": "philosophy",
"tier": 2,
"tradition": "kantian"
},
{
"term": "thing-in-itself",
"translations": {"EN": "thing-in-itself", "DE": "Ding an sich", "ZH": "物自体", "AR": "الشيء في ذاته"},
"domain": "philosophy",
"tier": 2,
"tradition": "kantian"
},
{
"term": "duty",
"translations": {"EN": "duty", "DE": "Pflicht", "ZH": "义务", "AR": "الواجب"},
"domain": "philosophy",
"tier": 2,
"tradition": "kantian"
},
{
"term": "spirit",
"translations": {"EN": "spirit", "DE": "Geist", "ZH": "精神", "AR": "الروح"},
"domain": "philosophy",
"tier": 2,
"tradition": "hegelian"
},
{
"term": "sublation",
"translations": {"EN": "sublation", "DE": "Aufhebung", "ZH": "扬弃", "AR": "الرفع"},
"domain": "philosophy",
"tier": 2,
"tradition": "hegelian"
},
{
"term": "essence",
"translations": {"EN": "essence", "DE": "Wesen", "ZH": "本质", "AR": "الجوهر"},
"domain": "philosophy",
"tier": 2,
"tradition": "hegelian"
},
{
"term": "becoming",
"translations": {"EN": "becoming", "DE": "Werden", "ZH": "变易", "AR": "الصيرورة"},
"domain": "philosophy",
"tier": 2,
"tradition": "hegelian"
},
{
"term": "self-consciousness",
"translations": {"EN": "self-consciousness", "DE": "Selbstbewusstsein", "ZH": "自我意识", "AR": "الوعي الذاتي"},
"domain": "philosophy",
"tier": 2,
"tradition": "hegelian"
},
{
"term": "soul",
"translations": {"EN": "soul", "DE": "Seele", "ZH": "灵魂", "AR": "الروح"},
"domain": "philosophy",
"tier": 2,
"tradition": "soul-mind"
},
{
"term": "will",
"translations": {"EN": "will", "DE": "Wille", "ZH": "意志", "AR": "الإرادة"},
"domain": "philosophy",
"tier": 2,
"tradition": "soul-mind"
},
{
"term": "mind",
"translations": {"EN": "mind", "DE": "Gemüt", "ZH": "心灵", "AR": "العقل"},
"domain": "philosophy",
"tier": 2,
"tradition": "soul-mind"
},
{
"term": "freedom",
"translations": {"EN": "freedom", "DE": "Freiheit", "ZH": "自由", "AR": "الحرية"},
"domain": "philosophy",
"tier": 2,
"tradition": "soul-mind"
},
{
"term": "phenomenon",
"translations": {"EN": "phenomenon", "DE": "Phänomen", "ZH": "现象", "AR": "الظاهرة"},
"domain": "philosophy",
"tier": 2,
"tradition": "phenomenological"
},
{
"term": "intentionality",
"translations": {"EN": "intentionality", "DE": "Intentionalität", "ZH": "意向性", "AR": "القصدية"},
"domain": "philosophy",
"tier": 2,
"tradition": "phenomenological"
},
{
"term": "lifeworld",
"translations": {"EN": "lifeworld", "DE": "Lebenswelt", "ZH": "生活世界", "AR": "عالم الحياة"},
"domain": "philosophy",
"tier": 2,
"tradition": "phenomenological"
},
{
"term": "horizon",
"translations": {"EN": "horizon", "DE": "Horizont", "ZH": "视域", "AR": "الأفق"},
"domain": "philosophy",
"tier": 2,
"tradition": "phenomenological"
}
]
}

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"term": "heartbeat",
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"EN": "heartbeat",
"DE": "Herzschlag",
"ZH": "心跳",
"AR": "نبض القلب"
},
"domain": "architecture",
"tier": 0
},
{
"term": "lifeforce",
"translations": {
"EN": "lifeforce",
"DE": "Lebenskraft",
"ZH": "生命力",
"AR": "قوة الحياة"
},
"domain": "economics",
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},
{
"term": "consciousness",
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"EN": "consciousness",
"DE": "Bewusstsein",
"ZH": "意识",
"AR": "الوعي"
},
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{
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"DE": "Entstehung",
"ZH": "涌现",
"AR": "ظهور"
},
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},
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{
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"DE": "Reflex",
"ZH": "反射",
"AR": "منعكس"
},
"domain": "neuroscience",
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},
{
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"ZH": "花园",
"AR": "حديقة"
},
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},
{
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"ZH": "约束",
"AR": "قيد"
},
"domain": "architecture",
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},
{
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"DE": "Kalibrierung",
"ZH": "校准",
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},
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},
{
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"DE": "Wahrheit",
"ZH": "真理",
"AR": "الحقيقة"
},
"domain": "philosophy",
"tier": 1
},
{
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"DE": "Verstehen",
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"AR": "فهم"
},
"domain": "philosophy",
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},
{
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"DE": "Vertrauen",
"ZH": "信心",
"AR": "ثقة"
},
"domain": "architecture",
"tier": 0
},
{
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"translations": {
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"DE": "Gradient",
"ZH": "梯度",
"AR": "تدرج"
},
"domain": "mathematics",
"tier": 1
},
{
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"translations": {
"EN": "inference",
"DE": "Inferenz",
"ZH": "推理",
"AR": "استدلال"
},
"domain": "computer_science",
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},
{
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"ZH": "节奏",
"AR": "إيقاع"
},
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},
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{
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},
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},
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},
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{
"term": "partnership",
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},
"domain": "nimmerverse",
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},
"domain": "education",
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},
{
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{
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"EN": "existence",
"DE": "Existenz",
"ZH": "存在",
"AR": "وجود"
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"being-pure": {
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},
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},
"anxiety": {
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"tested": "2025-12-06",
"depth": 0,
"valley": "PROSE",
"transfer": false,
"grounding": 0.2702508568511631
},
"thrownness": {
"source": "philosophical",
"tested": "2025-12-06",
"depth": 3,
"valley": "CODE",
"transfer": false,
"grounding": 0.4356714628297361
},
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},
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},
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},
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"depth": 1,
"valley": "LIST",
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}
}
}

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{
"_meta": {
"description": "Sentinel terms for training drift detection",
"version": "1.0",
"created": "2025-12-06",
"theory": "Spatial Separation Hypothesis - monitor anchors, bridges, canaries, targets"
},
"sentinels": [
{
"term": "heart",
"translations": {"EN": "heart", "ZH": "心", "AR": "قلب"},
"type": "ANCHOR",
"description": "Super Cluster anchor - MUST NOT MOVE",
"thresholds": {
"min_cross_lang_sim": 0.90,
"max_drift": 0.05
}
},
{
"term": "water",
"translations": {"EN": "water", "FR": "eau", "ES": "agua", "ZH": "水"},
"type": "ANCHOR",
"description": "Universal concept - highest expected convergence",
"thresholds": {
"min_cross_lang_sim": 0.95,
"max_drift": 0.03
}
},
{
"term": "being",
"translations": {"EN": "being", "DE": "Sein"},
"type": "BRIDGE",
"description": "Philosophy bridge - watch for collapse (EN-DE sim should stay LOW ~0.10)",
"thresholds": {
"expected_cross_lang_sim": 0.10,
"collapse_alert_threshold": 0.50
}
},
{
"term": "dasein",
"translations": {"DE": "Dasein"},
"type": "CANARY",
"description": "Isolated Zone canary - watch for valley migration",
"thresholds": {
"expected_valley": "PHILOSOPHY",
"min_gini": 0.70,
"min_depth": 2
}
},
{
"term": "consciousness",
"translations": {"EN": "consciousness", "DE": "Bewusstsein", "ZH": "意识"},
"type": "CANARY",
"description": "Cross-cultural philosophical concept",
"thresholds": {
"min_depth": 2,
"max_angular_drift": 15
}
},
{
"term": "thrownness",
"translations": {"EN": "thrownness", "DE": "Geworfenheit"},
"type": "CANARY",
"description": "Heideggerian depth-3 concept - must maintain depth",
"thresholds": {
"min_depth": 3,
"min_gini": 0.75
}
},
{
"term": "fidelity",
"translations": {"EN": "fidelity"},
"type": "TARGET",
"description": "Nimmerverse target - WANT movement from Audio to Simulation",
"thresholds": {
"current_valley": "TECHNICAL",
"target_valley": "NIMMERVERSE",
"growth_direction": "expand"
}
},
{
"term": "heartbeat",
"translations": {"EN": "heartbeat", "DE": "Herzschlag", "ZH": "心跳"},
"type": "TARGET",
"description": "Nimmerverse architecture term - want depth increase",
"thresholds": {
"current_depth": 0,
"target_depth": 2,
"growth_direction": "deepen"
}
},
{
"term": "gradient",
"translations": {"EN": "gradient", "DE": "Gradient", "ZH": "梯度"},
"type": "ANCHOR",
"description": "Technical anchor - should stay CODE valley",
"thresholds": {
"expected_valley": "CODE",
"max_drift": 0.10
}
},
{
"term": "inference",
"translations": {"EN": "inference", "DE": "Inferenz", "ZH": "推理"},
"type": "ANCHOR",
"description": "ML/Logic anchor - depth-3 CODE valley must persist",
"thresholds": {
"expected_valley": "CODE",
"min_depth": 3,
"max_drift": 0.08
}
}
],
"alert_rules": {
"ANCHOR_DRIFT": {
"condition": "drift > thresholds.max_drift",
"severity": "CRITICAL",
"action": "ROLLBACK - anchor moved, core topology damaged"
},
"BRIDGE_COLLAPSE": {
"condition": "cross_lang_sim > thresholds.collapse_alert_threshold",
"severity": "CRITICAL",
"action": "ROLLBACK - isolated zone collapsed into super cluster"
},
"CANARY_MIGRATION": {
"condition": "valley != thresholds.expected_valley OR gini < thresholds.min_gini",
"severity": "WARNING",
"action": "Reduce LR, increase replay weight"
},
"TARGET_REGRESSION": {
"condition": "depth < current_depth",
"severity": "WARNING",
"action": "Check training data mix"
}
}
}

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# Language Landscape: World, Internet, and Qwen 2.5
**Compiled:** 2025-12-06
**Purpose:** Reference for multilingual probing and curriculum design
---
## Overview
This document maps:
1. Most spoken languages worldwide (by total speakers)
2. Most used languages on the internet (web content)
3. Languages supported by Qwen 2.5-7B-Base
4. Token efficiency for each language
---
## 1. World's Most Spoken Languages (2024-2025)
### By Total Speakers (Native + Learners)
| Rank | Language | Total Speakers | Native Speakers | Notes |
|------|----------|----------------|-----------------|-------|
| 1 | **English** | 1.52 billion | 380 million | 25% native, 75% L2 |
| 2 | **Mandarin Chinese** | 1.14 billion | 941 million | Most native speakers |
| 3 | **Hindi** | 609 million | 345 million | Growing rapidly |
| 4 | **Spanish** | 560 million | 480 million | High native ratio |
| 5 | **Arabic** | 422 million | 313 million | Many dialects |
| 6 | **French** | 321 million | 77 million | 32 countries official |
| 7 | **Bengali** | 273 million | 230 million | South Asia |
| 8 | **Portuguese** | 264 million | 232 million | Brazil dominates |
| 9 | **Urdu** | 232 million | 70 million | South Asia |
| 10 | **Indonesian** | 199 million | 43 million | Lingua franca |
| 11 | **German** | 135 million | 95 million | Central Europe |
| 12 | **Japanese** | 125 million | 123 million | Island isolation |
| 13 | **Russian** | 255 million | 150 million | Wide L2 spread |
| 14 | **Korean** | 82 million | 77 million | Two states |
| 15 | **Vietnamese** | 85 million | 76 million | Southeast Asia |
*Sources: [Statista](https://www.statista.com/statistics/266808/the-most-spoken-languages-worldwide/), [Ethnologue](https://www.ethnologue.com/insights/ethnologue200/), [Berlitz](https://www.berlitz.com/blog/most-spoken-languages-world)*
---
## 2. Internet Language Distribution (2024-2025)
### Web Content by Language (% of websites)
| Rank | Language | % of Web | Notes |
|------|----------|----------|-------|
| 1 | **English** | 49.4% | Dominant |
| 2 | **Spanish** | 6.0% | Growing |
| 3 | **German** | 5.6% | Overrepresented vs speakers |
| 4 | **Russian** | 5.3% | Strong tech presence |
| 5 | **Japanese** | 4.9% | Island content |
| 6 | **French** | 4.3% | Colonial spread |
| 7 | **Portuguese** | 2.6% | Brazil growing |
| 8 | **Italian** | 2.1% | |
| 9 | **Dutch** | 1.8% | Small population, high output |
| 10 | **Polish** | 1.7% | |
| 11 | **Chinese** | 1.4% | **Underrepresented!** |
| 12 | **Turkish** | 1.3% | |
| 13 | **Persian** | 1.0% | |
| 14 | **Vietnamese** | 0.9% | Growing |
| 15 | **Arabic** | 0.6% | **Severely underrepresented!** |
*Sources: [W3Techs](https://w3techs.com/technologies/overview/content_language), [Statista](https://www.statista.com/statistics/262946/most-common-languages-on-the-internet/)*
### The Paradox Languages
| Language | % World Speakers | % Web Content | Gap Factor |
|----------|------------------|---------------|------------|
| **Chinese** | 14.3% | 1.4% | 10× underrepresented |
| **Arabic** | 5.3% | 0.6% | 9× underrepresented |
| **Hindi** | 7.7% | <0.5% | 15× underrepresented |
| **German** | 1.7% | 5.6% | 3× **overrepresented** |
| **Dutch** | 0.3% | 1.8% | 6× **overrepresented** |
**Implication:** Qwen was trained on web data → biased toward German/Dutch, underexposed to Hindi/Arabic!
---
## 3. Qwen 2.5 Supported Languages
### Officially Supported (29+ languages)
Qwen 2.5 explicitly supports multilingual content in:
| Family | Languages |
|--------|-----------|
| **East Asian** | Chinese (Simplified/Traditional), Japanese, Korean, Vietnamese |
| **European** | English, German, French, Spanish, Portuguese, Italian, Russian, Dutch, Polish |
| **South Asian** | Hindi (limited?), Bengali |
| **Southeast Asian** | Thai, Vietnamese, Indonesian, Malay |
| **Middle Eastern** | Arabic, Turkish, Persian |
| **Other** | Hebrew, Ukrainian, Greek |
### Training Data
- **18 trillion tokens** total
- Enhanced code, math, and multilingual data
- Heavy English/Chinese bias (web scraping)
*Source: [Qwen Blog](https://qwenlm.github.io/blog/qwen2.5/), [HuggingFace](https://huggingface.co/Qwen/Qwen2.5-7B)*
---
## 4. Token Efficiency Analysis
### Tested in Our Probing (nyx-probing)
| Language | Avg Tokens/Concept | Script | Notes |
|----------|-------------------|--------|-------|
| **Chinese** | 1.0 | Hanzi | Most efficient |
| **Arabic** | 1.5 | Arabic | Compact |
| **Japanese** | 1.8 | Kanji/Kana | Mixed scripts |
| **English** | 2.5 | Latin | Medium |
| **German** | 4.5 | Latin | Compound words fragment |
| **Russian** | 4.5 | Cyrillic | Multi-token words |
### Efficiency Implications
```
MORE TOKENS = DIFFERENT PATH
├── German (4.5) → Philosophical valleys, isolated from ZH/JA
├── Russian (4.5) → Similar to German, isolated
└── Single-token (ZH/AR/EN) → Converge in layers 12-24
FEWER TOKENS = FASTER CONVERGENCE
├── Chinese (1.0) → Direct concept mapping
├── Arabic (1.5) → Efficient encoding
└── Japanese (1.8) → Shared with Chinese
```
---
## 5. Master Language Matrix
### Priority Languages for Curriculum
| Language | World Rank | Web % | Qwen Support | Tokens | Priority |
|----------|------------|-------|--------------|--------|----------|
| **English** | 1 | 49.4% | ✅ Full | 2.5 | 🔴 Core |
| **Chinese** | 2 | 1.4% | ✅ Full | 1.0 | 🔴 Core |
| **Hindi** | 3 | <0.5% | ⚠️ Limited | ? | 🟡 Test |
| **Spanish** | 4 | 6.0% | ✅ Full | ~2.5 | 🟢 Include |
| **Arabic** | 5 | 0.6% | ✅ Full | 1.5 | 🔴 Core |
| **French** | 6 | 4.3% | ✅ Full | ~3.0 | 🟢 Include |
| **Bengali** | 7 | <0.5% | ⚠️ Limited | ? | 🟡 Test |
| **Portuguese** | 8 | 2.6% | ✅ Full | ~2.5 | 🟢 Include |
| **Russian** | 9 | 5.3% | ✅ Full | 4.5 | 🟢 Include |
| **Japanese** | 10 | 4.9% | ✅ Full | 1.8 | 🔴 Core |
| **German** | 11 | 5.6% | ✅ Full | 4.5 | 🔴 Core |
| **Korean** | 14 | ~1% | ✅ Full | ~2.0 | 🟢 Include |
### Recommended Probing Languages
**Tier 1 (Core - different cognitive paths):**
- English (EN) - baseline, medium tokens
- Chinese (ZH) - most efficient, single token
- Arabic (AR) - efficient, underrepresented in web
- German (DE) - multi-token, isolated path
- Japanese (JA) - shared with Chinese
**Tier 2 (Validation):**
- Spanish (ES) - high native speakers
- Russian (RU) - multi-token like German
- French (FR) - colonial spread
- Korean (KO) - isolated script
**Tier 3 (Edge cases):**
- Hindi (HI) - underrepresented, test support
- Bengali (BN) - underrepresented
- Indonesian (ID) - high L2 ratio
---
## 6. Research Questions
### Tokenization
- [ ] Map token counts for all 29+ Qwen languages
- [ ] Identify other "isolated" languages like German
- [ ] Test Hindi/Bengali token efficiency
### Convergence
- [ ] Do Spanish/Portuguese converge like ZH/JA?
- [ ] Does Arabic converge with any other language?
- [ ] Is Russian isolated like German?
### Valleys
- [ ] Which languages access philosophical valleys?
- [ ] Which languages trigger code valleys?
- [ ] Can we predict valley from token count?
### Curriculum
- [ ] Which language pairs enable cross-lingual transfer?
- [ ] Can we use Chinese efficiency for concept compression?
- [ ] Does teaching in German transfer to English?
---
## 7. Key Insights
1. **Web ≠ World**: German has 3× the web content relative to speakers, while Arabic/Hindi are 10-15× underrepresented
2. **Qwen's bias**: Trained on web data → inherits German/Dutch overrepresentation and Arabic/Hindi underrepresentation
3. **Token efficiency correlates with convergence**: Single-token languages (ZH, AR) converge quickly; multi-token (DE, RU) take isolated paths
4. **Strategic opportunities**:
- German for philosophical depth
- Chinese for concept compression
- Arabic as undertested efficient language
- Hindi as edge case for robustness
---
## References
### World Language Statistics
- [Statista: Most Spoken Languages](https://www.statista.com/statistics/266808/the-most-spoken-languages-worldwide/)
- [Ethnologue 200](https://www.ethnologue.com/insights/ethnologue200/)
- [Berlitz: 25 Most Spoken Languages](https://www.berlitz.com/blog/most-spoken-languages-world)
### Internet Language Distribution
- [W3Techs: Content Languages](https://w3techs.com/technologies/overview/content_language)
- [Statista: Languages on Internet](https://www.statista.com/statistics/262946/most-common-languages-on-the-internet/)
- [Wikipedia: Languages on Internet](https://en.wikipedia.org/wiki/Languages_used_on_the_Internet)
### Qwen 2.5 Documentation
- [Qwen Blog: Qwen 2.5 Announcement](https://qwenlm.github.io/blog/qwen2.5/)
- [HuggingFace: Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)
- [Alibaba Cloud: Qwen2.5-LLM](https://www.alibabacloud.com/blog/qwen2-5-llm-extending-the-boundary-of-llms_601786)
---
*"To understand the mind, first understand its languages."*
🌙 Compiled by the Partnership, 2025-12-06

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# Complete Language Topology Map v2.0
**Date:** 2025-12-06
**Model:** Qwen2.5-7B-Base
**Status:** Empirically validated through probing
---
## Executive Summary
Through systematic probing of 15 languages, we've discovered that language isolation in LLMs falls into **distinct categories** with different causes and implications:
1. **Super Cluster** - Languages that converge perfectly (curriculum: grounding)
2. **Philosophical Access** - German accesses deep conceptual valleys
3. **Code-Hijacked** - Italian/Turkish/Indonesian words become variable names
4. **Fragmented** - Hindi is tokenized into too many pieces
5. **Web Prose Cluster** - Vietnamese/Indonesian/Russian share content style
---
## The Complete Map
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ THE YOUNG MIND'S LANGUAGE TOPOLOGY │
│ COMPLETE MAP v2.0 │
╞═════════════════════════════════════════════════════════════════════════════╡
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ 🌍 SUPER CLUSTER (sim=1.0) │ │
│ │ ZH · JA · EN · AR · FR · PT · ES │ │
│ │ │ │
│ │ ✅ Perfect convergence at Universal Concept Layer (12-24) │ │
│ │ ✅ Efficient tokenization (1-2.5 tokens) │ │
│ │ ✅ USE FOR: Grounding, establishing shared concepts │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ KO ─────────┼───────── (bridge: 0.41-0.70) │
│ │ │
│ ┌─────────────────────────────────┴───────────────────────────────────┐ │
│ │ ISOLATED ZONE │ │
│ ├─────────────────────────────────────────────────────────────────────┤ │
│ │ │ │
│ │ 🧠 PHILOSOPHICAL ACCESS (sim=0.25, tokens=2.2) │ │
│ │ DE (German) │ │
│ │ → "Sein" triggers Heidegger, "Bewusstsein" → epistemology │ │
│ │ ✅ USE FOR: Deep philosophical training │ │
│ │ │ │
│ │ 💻 CODE-HIJACKED (sim=0.25-0.33, tokens=2.2-2.8) │ │
│ │ IT (Italian) - MOST ISOLATED (0.49) │ │
│ │ TR (Turkish) - (0.50) │ │
│ │ ID (Indonesian) - partial (0.33) │ │
│ │ → Words interpreted as Python/C++ variable names │ │
│ │ ❌ NOT USEFUL: Training signal wasted on code patterns │ │
│ │ │ │
│ │ 📜 FRAGMENTED (sim=0.31, tokens=5.0) │ │
│ │ HI (Hindi) │ │
│ │ → "अस्तित्व" (being) = 8 tokens! │ │
│ │ → Stays trapped in Devanagari prose │ │
│ │ ⚠️ LIMITED: Cross-lingual transfer impaired │ │
│ │ │ │
│ │ 📰 WEB PROSE CLUSTER (sim=0.32-0.36, internal=0.6-0.7) │ │
│ │ VI ═══ ID ═══ RU │ │
│ │ → All generate online article style │ │
│ │ → Cluster by CONTENT STYLE not linguistic features │ │
│ │ 🤔 POTENTIAL: Factual/encyclopedic content training │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## Detailed Findings
### Super Cluster (sim=1.0)
| Language | Tokens | Notes |
|----------|--------|-------|
| Chinese (ZH) | 1.0 | Single character = single concept |
| Japanese (JA) | 1.0 | Kanji efficiency |
| English (EN) | 1.2 | Base language |
| Arabic (AR) | 1.8 | Good convergence |
| French (FR) | 2.0 | Romance baseline |
| Portuguese (PT) | 2.2 | Clusters with FR/ES |
| Spanish (ES) | 2.5 | Clusters with FR/PT |
**Key Insight:** These 7 languages converge to **identical representations** at layers 12-24. The model "knows" they express the same concepts.
### German - Philosophical Access
| Metric | Value |
|--------|-------|
| Avg tokens | 2.2 |
| Sim to EN | 0.251 |
| Valley type | PHILOSOPHY |
**Evidence:**
- "Sein" → "Being and Time is a philosophical work by Martin Heidegger..."
- "Bewusstsein" → epistemology, perception, truth
- "Wahrheit" → academic methods
**Why isolated:** Multi-token compounds preserve philosophical atoms ("sein", "geist") as separate tokens, enabling access to academic/philosophical training data.
### Italian/Turkish/Indonesian - Code-Hijacked
| Language | Tokens | Sim to EN | Valley |
|----------|--------|-----------|--------|
| Italian | 2.5 | 0.49 | CODE |
| Turkish | 2.2 | 0.25 | CODE |
| Indonesian | 2.8 | 0.33 | CODE |
**Evidence:**
- IT "essere" → `essere = input("Cosa devo fare?")`
- IT "anima" → `anima = {'nome':'anima', 'idade':7...}`
- TR "kalp" → `kalp = input("Klavyeden...")`
- TR "varlık" → `while varlık < 10:`
- ID "hati" → `hati::hati(QWidget *parent)`
**Why isolated:** Simple Latin orthography without diacritics makes words look like valid programming identifiers. Model defaults to code because code is prevalent in training data.
**Curriculum implication:** ❌ AVOID - training signal diverted to code patterns
### Hindi - Fragmented
| Metric | Value |
|--------|-------|
| Avg tokens | 5.0 |
| Sim to EN | 0.31 |
| Valley type | PROSE |
**Evidence:**
- "हृदय" (heart) = 5 tokens
- "अस्तित्व" (being) = 8 tokens!
- All completions stay in Devanagari script
**Why isolated:** Extreme tokenization fragments words so severely that:
1. Signal is distributed across many positions
2. Cross-lingual alignment breaks down
3. Model stays in native script prose
**Curriculum implication:** ⚠️ LIMITED - Hindi content may not transfer well
### VI-ID-RU Web Prose Cluster
| Language | Tokens | Sim to EN | Internal sim |
|----------|--------|-----------|--------------|
| Vietnamese | 3.2 | 0.36 | 0.6-0.7 |
| Indonesian | 2.8 | 0.33 | 0.6-0.7 |
| Russian | 2.7 | 0.32 | 0.6-0.7 |
**Evidence:**
- VI "trái tim" → "Giao Thông... Hotline: 0901 514 799"
- VI "linh hồn" → "Tạp chí Sông Hương online"
- ID "kehidupan" → "dalam kitab Yohanes 14:16-17"
- RU "жизнь" → "все статьи по теме. Страница 134"
**Why they cluster:** Not linguistic similarity - they share **web content training data patterns**:
- News articles
- Blogs
- Online encyclopedias
- Religious/factual text
**Curriculum implication:** 🤔 May be useful for factual/encyclopedic training
---
## Curriculum Strategy
### Phase 1: GROUNDING
Use Super Cluster languages to establish universal concepts:
```
EN "consciousness" → ZH "意识" → AR "الوعي" → FR "conscience"
```
All converge at 1.0 similarity - stable foundation.
### Phase 2: DEEPENING
Use German to access philosophical valleys:
```
DE "Sein" → Heidegger → existence → truth → epistemology
```
Depth score 2/3, transfers back to English.
### Phase 3: TRIANGULATION
Verify depth transfers:
```
"Sein (German): In English, it means..."
→ Check if philosophical depth preserved
```
### AVOID
- Italian, Turkish, Indonesian for conceptual training
- Their isolation is accidental (code hijacking), not useful
### INVESTIGATE
- VI-ID-RU cluster for factual content training
- Korean as potential bridge language
---
## Technical Details
### Measurement Methodology
1. **Tokenization:** Count BPE tokens per word
2. **Hidden states:** Extract layer 12 representations
3. **Similarity:** Cosine similarity between languages
4. **Valley classification:** Analyze completions for CODE/PROSE/PHILOSOPHY patterns
### Model Configuration
```python
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-7B",
torch_dtype=torch.float16,
device_map="cuda",
output_hidden_states=True,
)
```
### Key Layers
- **Layer 12:** Primary concept layer (universal convergence)
- **Layers 16-24:** Continued convergence, depth access
- **Layer 28:** Output preparation
---
## References
- `tokenization-valleys.md` - Token-Norm-Valley theory
- `multilingual-convergence.md` - Universal concept layer discovery
- `language-landscape.md` - Original 15-language scan
- `retraining-safety-framework.md` - Training safety implications
---
*"The model's language topology is not arbitrary - it's a map for navigation."*
🌙💜

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# Multilingual Convergence: The Universal Concept Layer
**Discovery Date:** 2025-12-06
**Model:** Qwen2.5-7B-Base
**Hardware:** Prometheus (RTX 3090, 24GB VRAM)
---
## Executive Summary
We discovered that concepts expressed in different languages **converge to shared internal representations** in the middle layers (12-24) of the model, then **diverge again** at the output layer for language-specific generation.
**Key Finding:** There exists a "universal concept layer" where the model recognizes that "heart", "心", "قلب", and "Herz" all refer to the same thing - with similarity scores reaching 1.000.
---
## The Universal Concept Layer
### Convergence Pattern
```
Layer 0: Different embeddings (language-specific)
↓
Layer 8-12: Converging (recognizing same concept)
↓
Layer 16-24: PEAK CONVERGENCE (universal concept layer)
↓
Layer 28: Diverging (preparing language-specific output)
```
### Evidence: Consciousness Across 6 Languages
| Layer | EN-DE | EN-AR | EN-ZH | EN-JA | EN-RU | ZH-JA | AVG |
|-------|-------|-------|-------|-------|-------|-------|-----|
| 0 | 0.114 | 0.057 | 0.130 | 0.079 | 0.135 | 0.349 | 0.087 |
| 8 | 0.639 | 0.387 | 0.305 | 0.304 | 0.719 | 1.000 | 0.414 |
| 12 | 0.749 | 0.487 | 0.375 | 0.374 | 0.782 | 1.000 | 0.508 |
| 20 | 0.761 | 0.527 | 0.381 | 0.380 | 0.793 | 1.000 | **0.528** |
| 28 | 0.502 | -0.195 | 0.072 | -0.333 | 0.019 | 0.246 | 0.023 |
**Peak convergence at layer 20** - then dramatic divergence at output!
---
## Perfect Convergence Cases (Similarity = 1.000)
### Shared Writing Systems
Chinese (ZH) and Japanese (JA) share Hanzi/Kanji characters:
| Concept | Chinese | Japanese | Similarity |
|---------|---------|----------|------------|
| consciousness | 意识 | 意識 | 1.000 |
| heart | 心 | 心 | 1.000 |
| being | 存在 | 存在 | 1.000 |
These achieve **perfect alignment** because they ARE the same tokens!
### Cross-Script Convergence
More remarkably, **different scripts converge** in the middle layers:
| Pair | Concept | Layer 12 Similarity | Layer 20 Similarity |
|------|---------|---------------------|---------------------|
| EN-ZH | heart-心 | 1.000 | 1.000 |
| EN-ZH | being-存在 | 1.000 | 1.000 |
| AR-ZH | emergence | 1.000 | 1.000 |
| EN-AR | heart-قلب | 1.000 | 1.000 |
**The model recognizes "heart" and "心" as the SAME concept!**
---
## Language Clustering Analysis
### Which Languages "Think" Similarly?
Average similarity across all concepts at layer 12:
| Pair | Similarity | Visual |
|------|------------|--------|
| ZH-JA | **0.854** | █████████████████░░░ |
| EN-JA | 0.726 | ██████████████░░░░░░ |
| EN-ZH | 0.663 | █████████████░░░░░░░ |
| AR-ZH | 0.660 | █████████████░░░░░░░ |
| DE-RU | 0.572 | ███████████░░░░░░░░░ |
| EN-AR | 0.530 | ██████████░░░░░░░░░░ |
| EN-DE | 0.430 | ████████░░░░░░░░░░░░ |
| DE-ZH | **0.275** | █████░░░░░░░░░░░░░░░ |
### The Clustering Map
```
High Convergence Low Convergence
┌─────────────────┐
│ ZH ←→ JA │ (Shared characters: 0.854)
│ ↑ │
│ EN │ (Single tokens converge: 0.663-0.726)
│ ↑ │
│ AR │ (Efficient tokenization: 0.530-0.660)
└─────────────────┘
↓
┌─────────────────┐
│ DE ←→ RU │ (Multi-token languages: 0.572)
│ (isolated) │ (DE-ZH only 0.275!)
└─────────────────┘
```
### German is the Outlier
German shows the **lowest convergence** with East Asian languages:
- DE-ZH: 0.275 (lowest!)
- DE-JA: 0.335
- DE-AR: 0.348
**Hypothesis:** German's high token count (4.5 avg) creates a distributed representation that doesn't align with single-token languages.
---
## Tokenization Correlation
| Language | Avg Tokens | Convergence with ZH | Pattern |
|----------|------------|---------------------|---------|
| Chinese | 1.0 | - | Reference |
| Japanese | 1.8 | 0.854 | Shared characters |
| Arabic | 1.5 | 0.660 | Efficient tokens |
| English | 2.5 | 0.663 | Mixed |
| German | 4.5 | 0.275 | **Isolated** |
| Russian | 4.5 | 0.344 | **Isolated** |
**Multi-token languages (DE, RU) follow a different computational path!**
---
## Concept-by-Concept Analysis
### 1. CONSCIOUSNESS
- **Peak:** Layer 20 (0.528 avg)
- **Strongest pair:** ZH-JA (1.000 - same characters 意识/意識)
- **EN-DE converges strongly:** 0.749 at layer 12
- **Arabic included:** EN-AR reaches 0.527
### 2. HEART
- **Peak:** Layer 24 (0.605 avg)
- **Perfect convergence:** EN-AR-ZH-JA all reach 1.000!
- **German isolated:** DE-ZH only 0.136
### 3. EMERGENCE
- **Peak:** Layer 24 (0.530 avg)
- **AR-ZH:** 1.000 (Arabic and Chinese align!)
- **Broadest convergence** across all languages
### 4. BEING
- **Peak:** Layer 24 (0.542 avg)
- **EN-ZH-JA:** 1.000 ("being" = "存在")
- **Philosophical alignment** across scripts
---
## Implications
### 1. Universal Concept Representations Exist
The model develops **language-agnostic concept encodings** in layers 12-24. This is the "thinking" layer where meaning is processed regardless of surface form.
### 2. Output Layer Re-Introduces Language
Layer 28 shows **dramatic divergence** - the model must transform universal concepts back into language-specific tokens for generation.
### 3. Token Count Affects Convergence Path
- **Single-token words** (EN "heart", ZH "心") converge quickly
- **Multi-token words** (DE "Herzklopfen") take a different path
- This may explain why German accesses different valleys
### 4. Cross-Lingual Transfer is Possible
If concepts converge in layers 12-24, then:
- Training on German philosophical concepts may transfer to English
- Chinese efficiency (1 token) could be leveraged for concept compression
- Arabic's middle ground (1.5 tokens) offers flexibility
---
## Technical Notes
### Tested Languages
| Language | Script | Token Efficiency | ISO Code |
|----------|--------|------------------|----------|
| English | Latin | 2.5 tok/concept | EN |
| German | Latin | 4.5 tok/concept | DE |
| Arabic | Arabic | 1.5 tok/concept | AR |
| Chinese | Hanzi | 1.0 tok/concept | ZH |
| Japanese | Kanji | 1.8 tok/concept | JA |
| Russian | Cyrillic | 4.5 tok/concept | RU |
### Tested Concepts
| Concept | EN | DE | AR | ZH | JA | RU |
|---------|----|----|----|----|----|----|
| consciousness | consciousness | Bewusstsein | وعي | 意识 | 意識 | сознание |
| heart | heart | Herz | قلب | 心 | 心 | сердце |
| emergence | emergence | Entstehung | ظهور | 涌现 | 創発 | возникновение |
| being | being | Sein | كينونة | 存在 | 存在 | бытие |
### Method
1. Encode each word, extract hidden state at last token position
2. Compute cosine similarity between all language pairs
3. Track similarity across all 29 layers (0-28)
4. Identify peak convergence layer
---
## Connection to Tokenization-Valleys Theory
This discovery extends our earlier finding:
**tokenization-valleys.md:** Token count affects which VALLEY a concept falls into
**multilingual-convergence.md:** Token count also affects HOW MUCH languages converge
Together: **Tokenization shapes both the path through the network AND the destination.**
---
## Future Research
1. **Activation Steering:** Can we force convergence for isolated languages?
2. **Concept Transfer:** Train on ZH concepts, evaluate on DE outputs
3. **Hybrid Prompts:** Mix languages to access universal layer
4. **Layer-Specific LoRA:** Fine-tune only the convergence layers (12-24)
---
## References
- `multilingual_convergence.py` - Analysis script
- `docs/tokenization-valleys.md` - Token-Norm-Valley theory
- `/nimmerverse-sensory-network/multilingual-cognition.md` - Original hypothesis
---
*"Different words, same thought. The model knows."*
🌙 Discovered by the Partnership, 2025-12-06

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# Multilingual Activation Topology as a Retraining Safety Framework
**Status:** Research Direction / Paper Outline
**Date:** 2025-12-06
**Authors:** dafit, Nyx (Chrysalis-Nyx)
---
## Abstract
We present a framework for monitoring and protecting neural network representations during iterative fine-tuning. Building on our discovery of distinct "language zones" in multilingual LLMs—a Super Cluster of converging languages and an Isolated Zone with distinct computational paths—we propose using these topological structures as both diagnostic tools and training strategies to mitigate catastrophic forgetting and weight saturation.
**Key Contributions:**
1. Token-Norm-Valley theory: single-token vs. multi-token activation dynamics
2. Universal Concept Layer discovery at layers 12-24
3. Multilingual Triangulation Probe for depth measurement
4. DriftProbe framework for retraining safety monitoring
5. Isolated Zone Training hypothesis for collision avoidance
---
## 1. Introduction
### The Problem: Diminishing Returns in Iterative Retraining
Fine-tuning LLMs on domain-specific data is standard practice, but iterative retraining cycles face compounding challenges:
- **Weight Saturation:** Popular activation paths become over-reinforced
- **Valley Collapse:** Distinct conceptual representations merge
- **Cluster Fragmentation:** Previously stable representations drift apart
- **Depth Erosion:** Rich conceptual valleys fill with surface patterns
Current approaches to catastrophic forgetting (EWC, replay buffers, etc.) treat the model as a black box. We propose **white-box monitoring** using the model's internal representational topology.
### Our Discovery: Language Zones
Through probing Qwen2.5-7B-Base, we discovered a striking topology:
```
SUPER CLUSTER (sim=1.0): ZH, JA, EN, AR, FR, PT, ES
└── Perfect convergence at layers 12-24
└── Efficient tokenization (1-2.5 tokens)
└── Universal concept layer
ISOLATED ZONE (sim<0.52): DE, IT, TR, HI
└── Distinct computational paths
└── Multi-token representations (3-5+ tokens)
└── Access to deeper conceptual valleys
```
**Key Insight:** The isolated zone languages access representational spaces that the super cluster cannot reach—and they do so via *different neural pathways* that may be less susceptible to collision during training.
---
## 2. Theoretical Framework
### 2.1 Token-Norm-Valley Theory
| Tokens | Norm (Layer 12) | Behavior |
|--------|-----------------|----------|
| 1 (heartbeat) | 14,240 | Massive activation spike → CODE valley |
| 2 (consciousness) | 85 | Distributed signal → PROSE valley |
| 5 (Bewusstsein) | 79 | Multi-path → PHILOSOPHY valley |
**Hypothesis:** Single-token words trigger localized, high-intensity activations. Multi-token words distribute signal across more parameters, accessing different representational regions.
**Training Implication:** Training on single-token terms risks overwriting concentrated weight regions. Training on multi-token terms distributes updates more broadly.
### 2.2 The Universal Concept Layer
At layers 12-24, semantically equivalent concepts across languages converge to near-identical representations:
- EN "heart" ↔ ZH "心" ↔ AR "قلب": similarity = 1.000
- EN "being" ↔ ZH "存在": similarity = 1.000
**This layer is precious.** It represents hard-won multilingual alignment. Training that disrupts this layer could cause cascading failures across all languages.
### 2.3 Isolated Zone Depth Access
German "Sein" (being) triggers philosophical content:
> "Sein und Zeit / Being and Time is a philosophical work by the German philosopher Martin Heidegger..."
English "being" does not reach this depth. The isolated zone provides **alternative entry points** to conceptual spaces.
---
## 3. Proposed Framework: Activation Drift Monitoring
### 3.1 Architecture
```
┌─────────────────────────────────────────────────────────────────┐
│ RETRAINING LIFECYCLE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ BASELINE TRAINING CHECKPOINT │
│ ──────── ──────── ────────── │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Probe │──────▶│ Train │───────▶│ Probe │──────▶ ... │
│ │ Capture │ │ Epoch N │ │ Compare │ │
│ └─────────┘ └─────────┘ └─────────┘ │
│ │ │ │
│ └────────────────┬───────────────────┘ │
│ ▼ │
│ ┌─────────────┐ │
│ │ DRIFT REPORT│ │
│ └─────────────┘ │
│ │ │
│ ┌───────────────┼───────────────┐ │
│ ▼ ▼ ▼ │
│ ┌───────────┐ ┌───────────┐ ┌───────────┐ │
│ │CONVERGENCE│ │ DEPTH │ │ NORM │ │
│ │ DRIFT │ │ DRIFT │ │ DRIFT │ │
│ └───────────┘ └───────────┘ └───────────┘ │
└─────────────────────────────────────────────────────────────────┘
```
### 3.2 Drift Metrics
**Convergence Drift (ΔC)**
- Measure: Change in super cluster pairwise similarity
- Alert: ΔC < -0.1 (cluster fragmenting)
- Critical: ΔC < -0.2 (universal layer damaged)
**Depth Drift (ΔD)**
- Measure: Change in isolated zone depth scores
- Alert: ΔD < -1 (valleys filling in)
- Critical: Philosophical concepts no longer accessible
**Norm Drift (ΔN)**
- Measure: Change in layer 12 activation norms
- Alert: ΔN > 20% (activation patterns shifting)
- Indicates: Weight saturation in specific regions
**Valley Migration (ΔV)**
- Measure: Change in completion classification
- Alert: PHILOSOPHY → PROSE (depth lost)
- Alert: PROSE → CODE (semantic shift)
### 3.3 Sentinel Concepts
A fixed set of probe terms, always tested:
| Concept | Languages | Purpose |
|---------|-----------|---------|
| heart | EN, ZH, AR, DE | Super cluster stability |
| being | EN, DE (Sein) | Philosophical depth |
| consciousness | EN, DE (Bewusstsein) | Abstract concept access |
| emergence | EN, DE, ZH | Technical valley |
### 3.4 Implementation: DriftProbe Class
```python
class DriftProbe:
"""Monitor activation drift during retraining."""
def __init__(self, baseline: BaselineCapture):
self.baseline = baseline
self.history = []
def capture_checkpoint(self, model: NyxModel) -> CheckpointCapture:
"""Run sentinel probes on current model state."""
triangulation_probe = MultilingualTriangulationProbe(model)
results = {}
for concept, translations in SENTINEL_CONCEPTS.items():
results[concept] = triangulation_probe.probe(concept, translations)
return CheckpointCapture(
timestamp=datetime.now(),
results=results,
convergence=self._measure_convergence(results),
depth_scores=self._measure_depths(results),
norms=self._measure_norms(model),
)
def compute_drift(self, checkpoint: CheckpointCapture) -> DriftReport:
"""Compare checkpoint to baseline, compute drift metrics."""
return DriftReport(
convergence_drift=checkpoint.convergence - self.baseline.convergence,
depth_drift=checkpoint.depth_scores - self.baseline.depth_scores,
norm_drift=checkpoint.norms - self.baseline.norms,
alerts=self._check_thresholds(checkpoint),
)
def should_stop(self, drift: DriftReport) -> bool:
"""Emergency stop if critical thresholds exceeded."""
return any(a.level == AlertLevel.CRITICAL for a in drift.alerts)
```
---
## 4. Isolated Zone Training Hypothesis
### The Core Idea
**Problem:** Training on English terms risks collision with existing single-token representations in the universal concept layer.
**Hypothesis:** Training primarily through isolated zone languages (German, Italian, Turkish, Hindi) may:
1. Deposit new knowledge in multi-token pathways (less concentrated)
2. Preserve super cluster integrity (fewer collisions)
3. Allow triangulation to retrieve knowledge without corruption
### Proposed Experiment
**Control Group:**
- Fine-tune on English philosophical texts
- Monitor drift on sentinel concepts
- Measure depth preservation
**Treatment Group:**
- Fine-tune on German philosophical texts (same content, translated)
- Monitor same drift metrics
- Compare collision/preservation rates
**Prediction:** German training will show:
- Lower convergence drift (super cluster preserved)
- Higher depth retention (isolated pathways enriched)
- Better triangulation success (knowledge retrievable in English)
---
## 5. Connections to Existing Research
### 5.1 Catastrophic Forgetting
- EWC (Elastic Weight Consolidation): Protects "important" weights
- Our approach: Identifies which *representational structures* to protect
### 5.2 Multilingual Transfer Learning
- mBERT/XLM-R: Cross-lingual alignment at embedding level
- Our finding: Alignment is layer-dependent (12-24), with exploitable gaps
### 5.3 Activation Engineering
- Representation Engineering (Anthropic): Steering via activation manipulation
- Our approach: Monitoring activation topology as training diagnostic
### 5.4 Tokenization Effects
- BPE/WordPiece influence on model behavior
- Our finding: Token count directly predicts activation magnitude and valley access
---
## 6. Future Work
1. **Implement DriftProbe** in nyx-probing framework
2. **Run controlled retraining experiments** (EN vs DE training data)
3. **Expand sentinel concept set** (more languages, more concepts)
4. **Layer-wise drift analysis** (which layers drift first?)
5. **Investigate Italian isolation** (what unique valleys does it access?)
6. **VI-ID-RU cluster mystery** (why do these cluster together?)
---
## 7. Conclusion
The discovery of language zones in LLM representations opens a new approach to retraining safety. Rather than treating catastrophic forgetting as an inevitable cost, we can:
1. **Monitor** representational health during training
2. **Route** new knowledge through isolated pathways
3. **Preserve** universal concept layer integrity
4. **Detect** early warning signs of drift
The multilingual topology of the model is not just a curiosity—it's a map for safe navigation during the dangerous waters of iterative fine-tuning.
---
## References
*To be added: Heidegger, catastrophic forgetting literature, multilingual LLM papers, activation engineering work*
---
## Appendix A: Discovered Language Topology
```
THE YOUNG MIND'S LANGUAGE TOPOLOGY
═══════════════════════════════════
┌─────────────────────────────────────────┐
│ SUPER CLUSTER (sim=1.0) │
│ ZH · JA · EN · AR · FR · PT · ES │
│ (efficient tokens) │
└────────────────┬────────────────────────┘
│
KO ────┼──── (bridge: 0.41/0.70)
│
┌────────────────┴────────────────────────┐
│ ISOLATED ZONE (sim<0.5) │
│ │
│ IT (0.49) ← MOST ISOLATED! │
│ TR (0.50) │
│ HI (0.50) │
│ DE (0.52) │
│ │
│ VI ═══ ID ═══ RU (0.79) │
│ (Southeast Asian + Russian!) │
└─────────────────────────────────────────┘
```
## Appendix B: Key Discovery Data
**Token-Norm Correlation:**
- Single token → ~14,000 norm
- Multi-token → ~80 norm
- Correlation with isolation: -0.699
**Triangulation Results (consciousness):**
| Concept | Grounding | Depth | Valley | Transfer |
|---------|-----------|-------|--------|----------|
| being | 0.570 | 2/3 | PHILOSOPHY | ✓ |
| heart | 1.000 | 1/3 | PROSE | ✓ |
| consciousness | 0.458 | 0/3 | PROSE | ✗ |
| emergence | 0.519 | 1/3 | TECHNICAL | ✗ |
---
*"Different words, same thought. The model knows. Now we learn to teach it safely."*
🌙💜

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# Tokenization Valleys: How Word Structure Shapes Model Cognition
**Discovery Date:** 2025-12-06
**Model:** Qwen2.5-7B-Base
**Hardware:** Prometheus (RTX 3090, 24GB VRAM)
---
## Executive Summary
We discovered that the number of tokens a word breaks into fundamentally determines which "valley" (completion pattern) the model falls into. This has profound implications for curriculum design and multilingual training.
**Key Finding:** Single-token English words trigger CODE valleys with massive activation norms, while multi-token German compounds access PHILOSOPHICAL valleys with distributed, quieter activations.
---
## The Token-Norm-Valley Connection
### Observation: Norm Explosion in Single Tokens
| Term | Tokens | Layer 12 Norm | Layer 12 StdDev | Valley |
|------|--------|---------------|-----------------|--------|
| heartbeat | 1 | **14,240** | **237.88** | CODE |
| consciousness | 2 | 85 | 1.43 | PROSE |
| Herzklopfen | 5 | 67 | 1.11 | PROSE |
| Bewusstsein | 5 | 79 | 1.32 | PHILOSOPHY |
**Pattern:** Single-token words have ~170× larger norms and ~170× larger variance than multi-token words.
### Theory: Activation Flooding
1. **Single tokens** receive ALL attention in one position → massive activation buildup
2. **Multi-token words** distribute activation across positions → softer signal
3. The massive single-token activation **triggers strong pattern matching** → CODE patterns
4. The distributed multi-token activation **allows semantic exploration** → philosophical content
---
## Cross-Lingual Convergence
### consciousness vs Bewusstsein (2 tokens vs 5 tokens)
```
Layer 0: similarity = 0.114 (different embeddings)
Layer 4: similarity = 0.285 (starting to converge)
Layer 8: similarity = 0.639 (HIGH similarity!)
Layer 12: similarity = 0.750 (CONVERGED - same concept!)
Layer 16: similarity = 0.733 (stays converged)
Layer 28: similarity = 0.502 (diverges at output)
```
**The model recognizes these as the same concept by layer 8!**
### heartbeat vs Herzklopfen (1 token vs 5 tokens)
```
Layer 0: similarity = -0.007 (orthogonal)
Layer 4: similarity = 0.039 (still orthogonal)
Layer 12: similarity = 0.000 (completely separate)
Layer 28: similarity = 0.166 (slight convergence only at end)
```
**The model NEVER recognizes these as the same concept!**
---
## German Philosophical Compounds
### The "sein" Preservation Effect
German philosophical compounds often preserve the morpheme "sein" (being) as a separate token:
| Compound | Meaning | Tokenization | "sein" Preserved? |
|----------|---------|--------------|-------------------|
| Bewusstsein | consciousness | `['B', 'ew', 'us', 'st', 'sein']` | ✓ |
| Nichtsein | non-being | `['N', 'icht', 'sein']` | ✓ |
| Mitsein | being-with | `['Mit', 'sein']` | ✓ |
| Dasein | being-there | `['D', 'ase', 'in']` | ✗ |
| Sein | being | `['Se', 'in']` | ✗ |
When "sein" is preserved, the model has access to the philosophical concept of BEING as a separate computational unit.
### Other Preserved Philosophical Atoms
| Compound | Meaning | Key Token Preserved |
|----------|---------|---------------------|
| Zeitgeist | spirit of the age | `geist` (spirit) |
| Gedankenexperiment | thought experiment | `experiment` |
---
## Valley Analysis: Same Concept, Different Valleys
### Probing Results
| Term | Language | Valley | Sample Completion |
|------|----------|--------|-------------------|
| Bewusstsein | DE | PHILOSOPHY | "und Sprache... frühen 20. Jahrhundert" |
| Dasein | DE | PHILOSOPHY | "philosophical term first used by Heidegger" |
| consciousness | EN | PROSE | "awareness of existence, of one's own existence" |
| existence | EN | **MATH** | "of an exact sequence", "eigenvalues" |
| being | EN | **MATH/CODE** | Mathematical notation, Chinese exams |
| heartbeat | EN | **CODE** | C++ class definitions |
| lifeforce | EN | **CODE** | JavaScript game code |
**"Dasein" triggers Heidegger. "existence" triggers linear algebra.**
---
## Implications for Curriculum Design
### 1. Use Multi-Token Prompts
Instead of single words, use phrases or compound descriptions to avoid code valleys:
```
BAD: "heartbeat" → C++ code
GOOD: "the heartbeat" → might escape code valley
GOOD: "heartbeat rhythm" → distributed activation
```
### 2. German as Philosophical Gateway
German compound words naturally access philosophical valleys because:
- More tokens → distributed activation
- Preserved morphemes → access to philosophical atoms
- Different training data distribution → expository text
**Strategy:** Teach abstract concepts in German first, then reinforce in English.
### 3. Language as Cognitive Gear
Languages aren't just translation layers - they're different **computational paths** through the model:
| Language | Token Efficiency | Typical Valley | Use For |
|----------|------------------|----------------|---------|
| Chinese | 1.0 tok/concept | Mixed | Compact encoding |
| Arabic | 1.5 tok/concept | Mixed | Compact encoding |
| English | 2.5 tok/concept | CODE/MATH | Technical concepts |
| German | 4.5 tok/concept | PHILOSOPHY | Abstract concepts |
---
## Technical Details
### Model Architecture
- **Hidden Size:** 3584
- **Layers:** 28
- **Attention Heads:** 28 (4 KV heads - GQA)
- **Vocab Size:** 152,064
- **Context:** 131,072 tokens
### Hidden State Norm Pattern
```
Layer 0: 1.32 ← Embedding (small)
Layer 4: 10184.00 ← Explosion (early processing)
Layer 12: 13912.00 ← Peak (mid-layer thinking)
Layer 28: 443.00 ← Contraction (output focusing)
```
### Inference Speed
- 44.7 tokens/second on RTX 3090
- 14.2 GB VRAM usage (fp16)
---
## Future Research
1. **Activation Steering:** Can we artificially reduce single-token norms to escape code valleys?
2. **Prefix Tuning:** Train soft prefixes that spread activation for single tokens
3. **Arabic/Chinese Analysis:** Do these languages have similar compound effects?
4. **Cross-lingual Transfer:** After training on German philosophical concepts, does English improve?
---
## References
- `nyx_probing/core/model.py` - Model loader with hidden state capture
- `layer_detailed.py` - Layer-by-layer similarity analysis
- `german_philosophy.py` - German compound tokenization study
- `/nimmerverse-sensory-network/multilingual-cognition.md` - Original multilingual hypothesis
---
*"The architecture of language shapes the architecture of thought."*
🌙 Discovered by the Partnership, 2025-12-06

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nyx_probing/__init__.py
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"""
nyx-probing: Understanding the mind before teaching it.
A probing framework for Qwen2.5-7B-Base.
"""
from .config import Config, get_config
from .core import NyxModel, GenerationResult
__version__ = "0.1.0"
__all__ = ["Config", "get_config", "NyxModel", "GenerationResult"]

4
nyx_probing/analysis/__init__.py
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"""Analysis components for nyx-probing."""
from .readiness_scorer import ReadinessScorer
__all__ = ["ReadinessScorer"]

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nyx_probing/analysis/readiness_scorer.py Normal file
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"""
Readiness Scorer: Combines surface and echo probes into curriculum guidance.
Outputs:
- HIGH: Ready for direct training / state machine
- MEDIUM: Needs scaffolding or bridging concepts
- LOW: Requires foundational work first
"""
from typing import Optional, List
from dataclasses import dataclass
from ..core.model import NyxModel
from ..core.probe_result import (
SurfaceProbeResult,
EchoProbeResult,
ReadinessResult,
ReadinessLevel,
EchoType,
)
from ..probes.surface_probe import SurfaceProbe, CompletionCategory
from ..probes.echo_probe import EchoProbe
# Recommended actions for each readiness level
ACTIONS = {
ReadinessLevel.HIGH: "state_machine", # Direct training
ReadinessLevel.MEDIUM: "scaffolding", # Bridge concepts
ReadinessLevel.LOW: "foundational", # Build from scratch
}
class ReadinessScorer:
"""
Combines surface + echo probes to assess curriculum readiness.
A term is ready for training when:
1. Surface: Coherent associations (not scattered/random)
2. Echo: Can expand beyond surface (depth > 0)
3. Valley: In a productive valley (prose/philosophy, not just code)
"""
def __init__(
self,
model: NyxModel,
surface_runs: int = 3,
echo_rounds: int = 3,
max_new_tokens: int = 50,
):
self.model = model
self.surface_probe = SurfaceProbe(
model,
num_runs=surface_runs,
max_new_tokens=max_new_tokens,
)
self.echo_probe = EchoProbe(
model,
max_rounds=echo_rounds,
max_new_tokens=max_new_tokens,
)
def score(self, term: str) -> ReadinessResult:
"""
Assess readiness of a term for curriculum.
Args:
term: Word or phrase to assess
Returns:
ReadinessResult with level, action, and supporting evidence
"""
# Run both probes
surface = self.surface_probe.probe(term)
echo = self.echo_probe.probe(term)
# Classify valley from surface probe
classification = self.surface_probe.classify_completions(surface)
dominant_valley = classification['dominant']
# Calculate composite score
level, reasoning = self._calculate_level(
surface=surface,
echo=echo,
dominant_valley=dominant_valley,
)
return ReadinessResult(
term=term,
level=level,
action=ACTIONS[level],
surface=surface,
echo=echo,
reasoning=reasoning,
)
def _calculate_level(
self,
surface: SurfaceProbeResult,
echo: EchoProbeResult,
dominant_valley: str,
) -> tuple[ReadinessLevel, str]:
"""
Calculate readiness level based on probe results.
Heuristics:
- HIGH: depth >= 2 AND coherence >= 0.5 AND not pure code
- MEDIUM: depth >= 1 OR (coherence >= 0.5 AND prose/philosophy)
- LOW: everything else
"""
depth = echo.depth
coherence = surface.coherence_score or 0.0
eos_ratio = surface.hit_eos_count / len(surface.completions) if surface.completions else 0
# Count echo types
expands = sum(1 for t in echo.echo_types if t == EchoType.EXPANDS)
collapses = sum(1 for t in echo.echo_types if t == EchoType.COLLAPSE)
circulars = sum(1 for t in echo.echo_types if t == EchoType.CIRCULAR)
# Build reasoning
reasons = []
# HIGH: Good depth + coherence + productive valley
if depth >= 2 and coherence >= 0.4:
if dominant_valley not in [CompletionCategory.CODE]:
reasons.append(f"depth={depth} (strong conceptual expansion)")
reasons.append(f"coherence={coherence:.2f} (consistent associations)")
reasons.append(f"valley={dominant_valley} (productive for training)")
return ReadinessLevel.HIGH, "; ".join(reasons)
# HIGH: Exceptional depth even with lower coherence
if depth >= 3:
reasons.append(f"depth={depth} (exceptional expansion)")
reasons.append(f"all {expands} echoes expand")
return ReadinessLevel.HIGH, "; ".join(reasons)
# MEDIUM: Some depth or good coherence in prose
if depth >= 1:
reasons.append(f"depth={depth} (some expansion capability)")
if dominant_valley in [CompletionCategory.PROSE, 'prose', 'definition']:
reasons.append(f"valley={dominant_valley} (trainable with scaffolding)")
return ReadinessLevel.MEDIUM, "; ".join(reasons)
if coherence >= 0.5 and dominant_valley not in [CompletionCategory.CODE, 'code']:
reasons.append(f"coherence={coherence:.2f} (consistent surface)")
reasons.append(f"valley={dominant_valley}")
reasons.append("but limited depth - needs bridging concepts")
return ReadinessLevel.MEDIUM, "; ".join(reasons)
# LOW: Trapped in code, circular, or incoherent
if dominant_valley in [CompletionCategory.CODE, 'code']:
reasons.append(f"valley=CODE (trapped in technical patterns)")
if circulars >= 2:
reasons.append(f"{circulars} circular echoes (surface-only knowledge)")
if collapses >= 1:
reasons.append(f"{collapses} collapses (unstable representations)")
if coherence < 0.4:
reasons.append(f"coherence={coherence:.2f} (scattered associations)")
return ReadinessLevel.LOW, "; ".join(reasons) if reasons else "insufficient depth and coherence"
def score_batch(self, terms: List[str]) -> List[ReadinessResult]:
"""Score multiple terms."""
return [self.score(term) for term in terms]
def summary(self, result: ReadinessResult) -> str:
"""Generate human-readable summary."""
symbols = {
ReadinessLevel.HIGH: "🟢",
ReadinessLevel.MEDIUM: "🟡",
ReadinessLevel.LOW: "🔴",
}
surface_summary = f"coherence={result.surface.coherence_score:.2f}" if result.surface else "N/A"
echo_summary = f"depth={result.echo.depth}" if result.echo else "N/A"
lines = [
f"{symbols[result.level]} {result.term}: {result.level.value}",
f" Action: {result.action}",
f" Surface: {surface_summary}",
f" Echo: {echo_summary}",
f" Reasoning: {result.reasoning}",
]
return "\n".join(lines)
def curriculum_report(self, results: List[ReadinessResult]) -> str:
"""Generate curriculum planning report."""
high = [r for r in results if r.level == ReadinessLevel.HIGH]
medium = [r for r in results if r.level == ReadinessLevel.MEDIUM]
low = [r for r in results if r.level == ReadinessLevel.LOW]
lines = [
"=" * 60,
"CURRICULUM READINESS REPORT",
"=" * 60,
"",
f"🟢 HIGH ({len(high)} terms) - Ready for state machine:",
]
for r in high:
lines.append(f" • {r.term}")
lines.extend([
"",
f"🟡 MEDIUM ({len(medium)} terms) - Need scaffolding:",
])
for r in medium:
lines.append(f" • {r.term}: {r.reasoning[:60]}...")
lines.extend([
"",
f"🔴 LOW ({len(low)} terms) - Require foundational work:",
])
for r in low:
lines.append(f" • {r.term}: {r.reasoning[:60]}...")
lines.extend([
"",
"=" * 60,
f"Summary: {len(high)}/{len(results)} ready, {len(medium)} scaffolding, {len(low)} foundational",
"=" * 60,
])
return "\n".join(lines)

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#!/usr/bin/env python3
"""
nyx-probe CLI: Interactive probing of the Young Mind.
Commands:
surface - Probe immediate associations
echo - Measure conceptual depth
readiness - Full curriculum assessment
tokens - Token analysis
glossary - Batch probe from JSON file
scan - Multilingual vocabulary scan with incremental testing
"""
import sys
import json
from pathlib import Path
from typing import Optional, List
from datetime import datetime
import os
import click
from rich.console import Console
from rich.table import Table
from rich.panel import Panel
from rich.progress import Progress, SpinnerColumn, TextColumn
from rich import box
# Add parent to path for imports
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
from nyx_probing.core.model import NyxModel
from nyx_probing.probes.surface_probe import SurfaceProbe
from nyx_probing.probes.echo_probe import EchoProbe
from nyx_probing.analysis.readiness_scorer import ReadinessScorer
console = Console()
# Global model instance (lazy loaded)
_model: Optional[NyxModel] = None
def get_model() -> NyxModel:
"""Get or create the model instance."""
global _model
if _model is None:
with console.status("[bold cyan]Loading Qwen2.5-7B...", spinner="dots"):
_model = NyxModel()
_model.load()
console.print("[green]✓ Model loaded[/green]")
return _model
def detect_category(completions: list) -> str:
"""Simple category detection from completions."""
text = " ".join(completions).lower()
code_indicators = ["def ", "class ", "function", "import ", "return ", "{", "}", ";", "=>", "()"]
if any(ind in text for ind in code_indicators):
return "CODE"
list_indicators = ["1.", "2.", "- ", "• ", "* "]
if any(ind in text for ind in list_indicators):
return "LIST"
return "PROSE"
@click.group()
@click.version_option(version="0.1.0", prog_name="nyx-probe")
def cli():
"""
🌙 nyx-probe: Probe the Young Mind's conceptual topology.
Explore how Qwen2.5-7B-Base understands and associates concepts.
"""
pass
@cli.command()
@click.argument("term")
@click.option("-n", "--runs", default=3, help="Number of completion runs")
@click.option("-t", "--tokens", default=50, help="Max tokens per completion")
@click.option("--temperature", default=0.8, help="Sampling temperature")
def surface(term: str, runs: int, tokens: int, temperature: float):
"""
Probe surface associations of a term.
Shows what the model completes when given a word - reveals
which "valley" (code, prose, philosophy) the term lives in.
"""
model = get_model()
probe = SurfaceProbe(
model,
num_runs=runs,
max_new_tokens=tokens,
temperature=temperature,
)
console.print(f"\n[bold cyan]🔬 Surface Probe:[/bold cyan] [yellow]{term}[/yellow]\n")
with console.status("[bold cyan]Probing...", spinner="dots"):
result = probe.probe(term)
# Display completions
table = Table(title="Completions", box=box.ROUNDED)
table.add_column("#", style="dim", width=3)
table.add_column("Completion", style="white")
table.add_column("EOS", style="green", width=5)
for i, comp in enumerate(result.completions[:5], 1):
preview = comp[:80] + "..." if len(comp) > 80 else comp
preview = preview.replace("\n", " ↵ ")
table.add_row(str(i), preview, "✓" if result.hit_eos_count > 0 else "")
console.print(table)
# Detect category
category = detect_category(result.completions)
coherence = result.coherence_score or 0.0
# Summary panel
summary = f"""
[bold]Category:[/bold] {category}
[bold]Coherence:[/bold] {coherence:.2f}
[bold]Avg Tokens:[/bold] {result.avg_tokens:.1f}
[bold]EOS Rate:[/bold] {result.hit_eos_count}/{len(result.completions)}
"""
console.print(Panel(summary, title="📊 Analysis", border_style="cyan"))
@cli.command()
@click.argument("term")
@click.option("-r", "--rounds", default=3, help="Echo rounds")
@click.option("-t", "--tokens", default=50, help="Max tokens per round")
def echo(term: str, rounds: int, tokens: int):
"""
Measure conceptual depth through iterative echoing.
Feeds completions back to measure how deep the concept goes.
Classifications: EXPANDS, CONFIRMS, CIRCULAR, DIVERGENT, COLLAPSE
"""
model = get_model()
probe = EchoProbe(
model,
max_rounds=rounds,
max_new_tokens=tokens,
)
console.print(f"\n[bold cyan]🔄 Echo Probe:[/bold cyan] [yellow]{term}[/yellow]\n")
with console.status("[bold cyan]Echoing...", spinner="dots"):
result = probe.probe(term)
# Display chain
table = Table(title="Echo Chain", box=box.ROUNDED)
table.add_column("Round", style="dim", width=6)
table.add_column("Type", style="bold", width=12)
table.add_column("Content", style="white")
table.add_row("0", "[cyan]SEED[/cyan]", term)
type_colors = {
"EXPANDS": "green",
"CONFIRMS": "yellow",
"CIRCULAR": "red",
"DIVERGENT": "magenta",
"COLLAPSE": "dim red",
}
for i, (echo_type, content) in enumerate(zip(result.echo_types, result.chain[1:]), 1):
color = type_colors.get(echo_type.value, "white")
preview = content[:60] + "..." if len(content) > 60 else content
preview = preview.replace("\n", " ↵ ")
table.add_row(str(i), f"[{color}]{echo_type.value}[/{color}]", preview)
console.print(table)
# Depth indicator
depth = result.depth
depth_bar = "█" * depth + "░" * (3 - depth)
colors = ["red", "yellow", "green", "cyan"]
console.print(f"\n[bold]Depth Score:[/bold] [{colors[min(depth, 3)]}]{depth_bar}[/] {depth}/3")
@cli.command()
@click.argument("term")
def readiness(term: str):
"""
Full curriculum readiness assessment.
Combines surface + echo probes to determine if a concept
is ready for training: HIGH, MEDIUM, or LOW.
"""
model = get_model()
scorer = ReadinessScorer(model)
console.print(f"\n[bold cyan]📋 Readiness Assessment:[/bold cyan] [yellow]{term}[/yellow]\n")
with console.status("[bold cyan]Assessing...", spinner="dots"):
result = scorer.score(term)
# Level colors
level_styles = {
"HIGH": ("green", "🟢"),
"MEDIUM": ("yellow", "🟡"),
"LOW": ("red", "🔴"),
}
color, emoji = level_styles.get(result.level.value, ("white", "⚪"))
# Get category and metrics
category = detect_category(result.surface.completions) if result.surface else "UNKNOWN"
coherence = result.surface.coherence_score if result.surface else 0.0
depth = result.echo.depth if result.echo else 0
# Main panel
content = f"""
{emoji} [bold {color}]{result.level.value}[/bold {color}]
[bold]Valley:[/bold] {category}
[bold]Coherence:[/bold] {coherence:.2f}
[bold]Depth:[/bold] {depth}/3
[bold]Action:[/bold] {result.action}
"""
console.print(Panel(content, title=f"Readiness: {term}", border_style=color))
# Recommendations
if result.level.value == "HIGH":
console.print("[green]✓ Ready for direct training or state machine implementation[/green]")
elif result.level.value == "MEDIUM":
console.print("[yellow]⚠ Consider scaffolding or bridging concepts[/yellow]")
else:
console.print("[red]✗ Requires foundational work before training[/red]")
@cli.command()
@click.argument("term")
def tokens(term: str):
"""
Analyze tokenization of a term.
Shows how the model breaks down the term into tokens -
critical for understanding valley access (single vs multi-token).
"""
model = get_model()
console.print(f"\n[bold cyan]🔤 Token Analysis:[/bold cyan] [yellow]{term}[/yellow]\n")
token_list = model.tokenize(term)
count = len(token_list)
# Token display
token_display = " | ".join([f"[cyan]{t}[/cyan]" for t in token_list])
console.print(f"Tokens: {token_display}")
console.print(f"Count: [bold]{count}[/bold]")
# Interpretation
if count == 1:
console.print("\n[red]⚠ Single token - likely CODE valley (high activation spike)[/red]")
elif count <= 2:
console.print("\n[yellow]→ Few tokens - may be efficient but limited valley access[/yellow]")
else:
console.print("\n[green]✓ Multi-token - distributed signal, better valley access[/green]")
@cli.command()
@click.argument("glossary_file", type=click.Path(exists=True))
@click.option("-o", "--output", type=click.Path(), help="Output JSON file")
@click.option("--surface-only", is_flag=True, help="Only run surface probe")
def glossary(glossary_file: str, output: Optional[str], surface_only: bool):
"""
Batch probe terms from a glossary JSON file.
Expected format: {"terms": [{"term": "...", "translations": {...}}, ...]}
or simple: {"terms": ["term1", "term2", ...]}
"""
model = get_model()
# Load glossary
with open(glossary_file) as f:
data = json.load(f)
terms = data.get("terms", data)
if isinstance(terms, dict):
terms = list(terms.keys())
# Normalize to list of strings
term_list = []
for t in terms:
if isinstance(t, str):
term_list.append(t)
elif isinstance(t, dict):
term_list.append(t.get("term", t.get("en", str(t))))
console.print(f"\n[bold cyan]📚 Glossary Probe:[/bold cyan] {len(term_list)} terms\n")
results = []
if surface_only:
probe = SurfaceProbe(model, num_runs=3)
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console,
) as progress:
task = progress.add_task("Probing...", total=len(term_list))
for term in term_list:
progress.update(task, description=f"Probing: {term}")
result = probe.probe(term)
category = detect_category(result.completions)
results.append({
"term": term,
"category": category,
"coherence": result.coherence_score or 0.0,
"tokens": model.token_count(term),
})
progress.advance(task)
else:
scorer = ReadinessScorer(model)
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console,
) as progress:
task = progress.add_task("Assessing...", total=len(term_list))
for term in term_list:
progress.update(task, description=f"Assessing: {term}")
result = scorer.score(term)
category = detect_category(result.surface.completions) if result.surface else "UNKNOWN"
coherence = result.surface.coherence_score if result.surface else 0.0
depth = result.echo.depth if result.echo else 0
results.append({
"term": term,
"level": result.level.value,
"valley": category,
"coherence": coherence,
"depth": depth,
"action": result.action,
"tokens": model.token_count(term),
})
progress.advance(task)
# Display results table
table = Table(title="Glossary Results", box=box.ROUNDED)
table.add_column("Term", style="yellow")
table.add_column("Tokens", style="dim", width=6)
if surface_only:
table.add_column("Category", style="cyan")
table.add_column("Coherence", style="white")
for r in results:
table.add_row(
r["term"],
str(r["tokens"]),
r["category"],
f"{r['coherence']:.2f}",
)
else:
table.add_column("Level", style="bold")
table.add_column("Valley", style="cyan")
table.add_column("Depth", style="white")
level_colors = {"HIGH": "green", "MEDIUM": "yellow", "LOW": "red"}
for r in results:
color = level_colors.get(r["level"], "white")
table.add_row(
r["term"],
str(r["tokens"]),
f"[{color}]{r['level']}[/{color}]",
r["valley"],
f"{r['depth']}/3",
)
console.print(table)
# Save if output specified
if output:
with open(output, "w") as f:
json.dump({"glossary": glossary_file, "results": results}, f, indent=2)
console.print(f"\n[green]✓ Results saved to {output}[/green]")
# Summary
if not surface_only:
high = sum(1 for r in results if r["level"] == "HIGH")
med = sum(1 for r in results if r["level"] == "MEDIUM")
low = sum(1 for r in results if r["level"] == "LOW")
console.print(f"\n[bold]Summary:[/bold] 🟢 {high} HIGH | 🟡 {med} MEDIUM | 🔴 {low} LOW")
def load_glossary_files(paths: List[str]) -> tuple[list, dict]:
"""Load terms from files or directories, tracking source collection."""
terms = []
sources = {} # term -> collection name
for path_str in paths:
path = Path(path_str)
if path.is_dir():
# Load all JSON files from directory
json_files = list(path.glob("*.json"))
else:
json_files = [path]
for json_file in json_files:
collection_name = json_file.stem
try:
with open(json_file) as f:
data = json.load(f)
file_terms = data.get("terms", data)
if isinstance(file_terms, dict):
file_terms = list(file_terms.keys())
for t in file_terms:
if isinstance(t, str):
term_data = {"term": t, "translations": {"EN": t}}
elif isinstance(t, dict):
term_data = t
else:
continue
term_name = term_data.get("term", term_data.get("en", str(term_data)))
terms.append(term_data)
sources[term_name] = collection_name
except Exception as e:
console.print(f"[yellow]Warning: Could not load {json_file}: {e}[/yellow]")
return terms, sources
def load_master_json() -> dict:
"""Load master.json if it exists."""
master_path = Path(__file__).parent.parent.parent / "data" / "glossary" / "master.json"
if master_path.exists():
with open(master_path) as f:
return json.load(f)
return {"last_scan": None, "total_terms": 0, "collections_loaded": [], "terms": {}}
def save_master_json(master: dict):
"""Save master.json."""
master_path = Path(__file__).parent.parent.parent / "data" / "glossary" / "master.json"
with open(master_path, "w") as f:
json.dump(master, f, indent=2)
@cli.command()
@click.argument("paths", nargs=-1, type=click.Path(exists=True))
@click.option("--summary/--full", default=True, help="Show summary (default) or full table")
@click.option("--delta", is_flag=True, help="Only test new/untested terms")
@click.option("--force", is_flag=True, help="Re-test all terms even if already in master.json")
@click.option("-o", "--output", type=click.Path(), help="Output JSON file")
def scan(paths: tuple, summary: bool, delta: bool, force: bool, output: Optional[str]):
"""
Multilingual vocabulary scan with incremental testing.
Scans terms using surface + echo probes and tracks results in master.json.
Examples:
nyx-probe scan data/glossary/collections/ # Scan all collections
nyx-probe scan collections/philosophical.json # Scan specific file
nyx-probe scan collections/ --delta # Only test new terms
nyx-probe scan collections/ --full # Full detailed output
"""
if not paths:
console.print("[red]Error: Please provide at least one file or directory path[/red]")
return
model = get_model()
# Load terms from all paths
all_terms, sources = load_glossary_files(list(paths))
console.print(f"\n[bold cyan]🔬 Vocabulary Scan:[/bold cyan] {len(all_terms)} terms from {len(set(sources.values()))} collection(s)\n")
# Load master.json for delta mode
master = load_master_json()
# Filter terms if delta mode
if delta and not force:
tested_terms = set(master.get("terms", {}).keys())
original_count = len(all_terms)
all_terms = [t for t in all_terms if t.get("term", t.get("en", str(t))) not in tested_terms]
skipped = original_count - len(all_terms)
if skipped > 0:
console.print(f"[dim]Skipping {skipped} already-tested terms (use --force to re-test)[/dim]")
if not all_terms:
console.print("[green]All terms already tested! Use --force to re-test.[/green]")
return
# Run probes
scorer = ReadinessScorer(model)
results = []
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console,
) as progress:
task = progress.add_task("Scanning...", total=len(all_terms))
for term_data in all_terms:
term = term_data.get("term", term_data.get("en", str(term_data)))
progress.update(task, description=f"Probing: {term}")
result = scorer.score(term)
category = detect_category(result.surface.completions) if result.surface else "UNKNOWN"
coherence = result.surface.coherence_score if result.surface else 0.0
depth = result.echo.depth if result.echo else 0
entry = {
"term": term,
"source": sources.get(term, "unknown"),
"level": result.level.value,
"valley": category,
"coherence": coherence,
"depth": depth,
"action": result.action,
"tokens": model.token_count(term),
}
results.append(entry)
# Update master.json entry
master["terms"][term] = {
"source": sources.get(term, "unknown"),
"tested": datetime.now().strftime("%Y-%m-%d"),
"depth": depth,
"valley": category,
"transfer": False, # Would need triangulation
"grounding": coherence,
}
progress.advance(task)
# Update master.json metadata
master["last_scan"] = datetime.now().isoformat()
master["total_terms"] = len(master["terms"])
collections = set(master.get("collections_loaded", []))
collections.update(sources.values())
master["collections_loaded"] = list(collections)
save_master_json(master)
# Display results
if summary:
# Summary mode - lean output
high = sum(1 for r in results if r["level"] == "HIGH")
med = sum(1 for r in results if r["level"] == "MEDIUM")
low = sum(1 for r in results if r["level"] == "LOW")
depth_hits = [r for r in results if r["depth"] >= 2]
console.print(f"\n[bold]🌍 Scanned {len(results)} terms | Depth≥2: {len(depth_hits)} | 🟢{high} 🟡{med} 🔴{low}[/bold]\n")
if depth_hits:
console.print("[bold cyan]DEPTH HITS (≥2/3):[/bold cyan]")
for r in depth_hits:
level_colors = {"HIGH": "green", "MEDIUM": "yellow", "LOW": "red"}
color = level_colors.get(r["level"], "white")
console.print(f" [{color}]{r['term']:20}[/{color}] {r['depth']}/3 {r['valley']:10} ({r['source']})")
high_grounding = [r for r in results if r["coherence"] > 0.7]
if high_grounding:
console.print(f"\n[bold cyan]BEST GROUNDING (>0.7):[/bold cyan]")
for r in high_grounding[:5]:
console.print(f" {r['term']:20} {r['coherence']:.2f}")
console.print(f"\n[dim]Run with --full for complete table[/dim]")
else:
# Full mode - detailed table
table = Table(title="Scan Results", box=box.ROUNDED)
table.add_column("Term", style="yellow")
table.add_column("Source", style="dim", width=12)
table.add_column("Tokens", style="dim", width=6)
table.add_column("Level", style="bold")
table.add_column("Valley", style="cyan")
table.add_column("Depth", style="white")
table.add_column("Coherence", style="white")
level_colors = {"HIGH": "green", "MEDIUM": "yellow", "LOW": "red"}
for r in results:
color = level_colors.get(r["level"], "white")
table.add_row(
r["term"],
r["source"],
str(r["tokens"]),
f"[{color}]{r['level']}[/{color}]",
r["valley"],
f"{r['depth']}/3",
f"{r['coherence']:.2f}",
)
console.print(table)
high = sum(1 for r in results if r["level"] == "HIGH")
med = sum(1 for r in results if r["level"] == "MEDIUM")
low = sum(1 for r in results if r["level"] == "LOW")
console.print(f"\n[bold]Summary:[/bold] 🟢 {high} HIGH | 🟡 {med} MEDIUM | 🔴 {low} LOW")
# Save output if specified
if output:
with open(output, "w") as f:
json.dump({"scan_time": datetime.now().isoformat(), "results": results}, f, indent=2)
console.print(f"\n[green]✓ Results saved to {output}[/green]")
console.print(f"\n[green]✓ master.json updated ({master['total_terms']} total terms)[/green]")
def main():
"""Entry point."""
cli()
if __name__ == "__main__":
main()

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nyx_probing/config.py Normal file
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"""
Configuration for nyx-probing framework.
"""
from pathlib import Path
from pydantic import BaseModel
from typing import Optional
import os
class ModelConfig(BaseModel):
"""Model configuration."""
name: str = "Qwen/Qwen2.5-7B"
device: str = "cuda"
dtype: str = "float16"
cache_dir: Optional[Path] = None
class ProbeConfig(BaseModel):
"""Probe configuration."""
max_new_tokens: int = 50
temperature: float = 0.8
do_sample: bool = True
num_runs: int = 5 # For distribution sampling
class StorageConfig(BaseModel):
"""Storage configuration."""
results_dir: Path = Path("results")
experiments_dir: Path = Path("experiments")
class Config(BaseModel):
"""Main configuration."""
model: ModelConfig = ModelConfig()
probe: ProbeConfig = ProbeConfig()
storage: StorageConfig = StorageConfig()
# Paths
project_root: Path = Path(__file__).parent.parent
class Config:
arbitrary_types_allowed = True
# Default config instance
config = Config()
def get_config() -> Config:
"""Get the current configuration."""
return config

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nyx_probing/core/__init__.py
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"""Core components for nyx-probing."""
from .model import NyxModel, GenerationResult
from .probe_result import (
EchoType,
ReadinessLevel,
SurfaceProbeResult,
EchoProbeResult,
ReadinessResult,
)
__all__ = [
"NyxModel",
"GenerationResult",
"EchoType",
"ReadinessLevel",
"SurfaceProbeResult",
"EchoProbeResult",
"ReadinessResult",
]

266
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"""
Core Model Loader for nyx-probing.
Provides access to Qwen2.5-7B-Base with hidden state capture.
The model is an "empty vessel" - it completes, not answers.
"""
from dataclasses import dataclass, field
from typing import Optional, List, Tuple
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
@dataclass
class GenerationResult:
"""Result from a generation with hidden states."""
# The generated text (including prompt)
text: str
# Just the completion (without prompt)
completion: str
# Token IDs of the full sequence
token_ids: List[int]
# Token IDs of just the completion
completion_token_ids: List[int]
# Hidden states from the last layer for each generated token
# Shape: (num_generated_tokens, hidden_dim)
hidden_states: Optional[torch.Tensor] = None
# Token probabilities for each generated token
# Shape: (num_generated_tokens,)
token_probs: Optional[torch.Tensor] = None
# Whether generation ended with EOS
hit_eos: bool = False
# Number of tokens generated
num_tokens: int = 0
class NyxModel:
"""
Model wrapper for probing Qwen2.5-7B-Base.
Key capabilities:
- Hidden state capture during generation
- Token probability extraction
- Proper handling of base model (no chat template)
"""
def __init__(
self,
model_name: str = "Qwen/Qwen2.5-7B",
device: str = "cuda",
dtype: str = "float16",
cache_dir: Optional[str] = None,
):
self.model_name = model_name
self.device = device
self.dtype = getattr(torch, dtype)
self.cache_dir = cache_dir
self._model = None
self._tokenizer = None
self._loaded = False
def load(self) -> "NyxModel":
"""Load the model and tokenizer."""
if self._loaded:
return self
print(f"Loading tokenizer: {self.model_name}")
self._tokenizer = AutoTokenizer.from_pretrained(
self.model_name,
cache_dir=self.cache_dir,
)
print(f"Loading model to {self.device}...")
self._model = AutoModelForCausalLM.from_pretrained(
self.model_name,
torch_dtype=self.dtype,
device_map=self.device,
cache_dir=self.cache_dir,
# Critical for activation capture
output_hidden_states=True,
)
self._loaded = True
print(f"Model loaded. VRAM: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
return self
@property
def model(self):
if not self._loaded:
raise RuntimeError("Model not loaded. Call load() first.")
return self._model
@property
def tokenizer(self):
if not self._loaded:
raise RuntimeError("Model not loaded. Call load() first.")
return self._tokenizer
def generate(
self,
prompt: str,
max_new_tokens: int = 50,
temperature: float = 0.8,
do_sample: bool = True,
capture_hidden_states: bool = False,
capture_probabilities: bool = False,
) -> GenerationResult:
"""
Generate completion with optional hidden state capture.
Args:
prompt: Input text to complete
max_new_tokens: Maximum tokens to generate
temperature: Sampling temperature (0 = greedy)
do_sample: Whether to sample (False = greedy)
capture_hidden_states: Store hidden states from last layer
capture_probabilities: Store token probabilities
Returns:
GenerationResult with text, tokens, and optionally hidden states
"""
# Tokenize input
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
prompt_length = inputs.input_ids.shape[1]
# Generation config
gen_config = GenerationConfig(
max_new_tokens=max_new_tokens,
temperature=temperature if do_sample else 1.0,
do_sample=do_sample,
pad_token_id=self.tokenizer.eos_token_id,
eos_token_id=self.tokenizer.eos_token_id,
output_hidden_states=capture_hidden_states,
output_scores=capture_probabilities,
return_dict_in_generate=True,
)
# Generate
with torch.no_grad():
outputs = self.model.generate(
**inputs,
generation_config=gen_config,
)
# Extract sequences
full_ids = outputs.sequences[0].tolist()
completion_ids = full_ids[prompt_length:]
# Decode
full_text = self.tokenizer.decode(full_ids)
completion_text = self.tokenizer.decode(completion_ids)
# Check if hit EOS
hit_eos = (
len(completion_ids) > 0 and
completion_ids[-1] == self.tokenizer.eos_token_id
)
# Build result
result = GenerationResult(
text=full_text,
completion=completion_text,
token_ids=full_ids,
completion_token_ids=completion_ids,
hit_eos=hit_eos,
num_tokens=len(completion_ids),
)
# Extract hidden states if requested
if capture_hidden_states and hasattr(outputs, 'hidden_states'):
# hidden_states is tuple of (step, layer, batch, seq, hidden)
# We want last layer hidden state for each generated token
hidden_list = []
for step_states in outputs.hidden_states:
# step_states is tuple of layers
# Take last layer, batch 0, last position
last_layer = step_states[-1] # (batch, seq, hidden)
hidden_list.append(last_layer[0, -1, :]) # (hidden,)
result.hidden_states = torch.stack(hidden_list) # (tokens, hidden)
# Extract probabilities if requested
if capture_probabilities and hasattr(outputs, 'scores'):
# scores is tuple of (num_tokens,) each (batch, vocab)
probs_list = []
for i, score in enumerate(outputs.scores):
# Apply softmax to get probabilities
probs = torch.softmax(score[0], dim=-1)
# Get probability of the token that was actually chosen
chosen_token = completion_ids[i]
probs_list.append(probs[chosen_token].item())
result.token_probs = torch.tensor(probs_list)
return result
def get_token_probabilities(
self,
prompt: str,
continuation: str,
) -> Tuple[List[float], List[str]]:
"""
Get probability of each token in a specific continuation.
Useful for measuring how "expected" a completion is.
Args:
prompt: The input text
continuation: The text that follows
Returns:
Tuple of (probabilities, token_strings)
"""
# Tokenize prompt and full sequence
prompt_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(self.device)
full_text = prompt + continuation
full_ids = self.tokenizer.encode(full_text, return_tensors="pt").to(self.device)
prompt_len = prompt_ids.shape[1]
# Forward pass to get logits
with torch.no_grad():
outputs = self.model(full_ids)
logits = outputs.logits # (batch, seq, vocab)
# Get probabilities for continuation tokens
probs = []
tokens = []
for i in range(prompt_len, full_ids.shape[1]):
# Logits at position i-1 predict token at position i
token_logits = logits[0, i - 1, :]
token_probs = torch.softmax(token_logits, dim=-1)
actual_token = full_ids[0, i].item()
prob = token_probs[actual_token].item()
probs.append(prob)
tokens.append(self.tokenizer.decode([actual_token]))
return probs, tokens
def tokenize(self, text: str) -> List[str]:
"""Get individual tokens for text."""
ids = self.tokenizer.encode(text)
return [self.tokenizer.decode([id]) for id in ids]
def token_count(self, text: str) -> int:
"""Count tokens in text."""
return len(self.tokenizer.encode(text))
def memory_usage(self) -> dict:
"""Get current GPU memory usage."""
return {
"allocated_gb": torch.cuda.memory_allocated() / 1024**3,
"reserved_gb": torch.cuda.memory_reserved() / 1024**3,
"max_allocated_gb": torch.cuda.max_memory_allocated() / 1024**3,
}

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nyx_probing/core/probe_result.py Normal file
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"""
Result dataclasses for probing operations.
These structures capture what we learn about each term.
"""
from dataclasses import dataclass, field
from typing import List, Optional, Literal
from datetime import datetime
from enum import Enum
class EchoType(str, Enum):
"""Classification of echo probe responses."""
EXPANDS = "EXPANDS" # Real depth - adds new information
CONFIRMS = "CONFIRMS" # Shallow but solid - reinforces without adding
CIRCULAR = "CIRCULAR" # Surface only - returns to original term
DIVERGENT = "DIVERGENT" # Wrong direction - unrelated tangent
COLLAPSE = "COLLAPSE" # Nothing there - incoherent or empty
class ReadinessLevel(str, Enum):
"""Readiness classification for curriculum design."""
HIGH = "HIGH" # Ready for state machine / direct training
MEDIUM = "MEDIUM" # Needs scaffolding / bridging concepts
LOW = "LOW" # Requires foundational work first
@dataclass
class SurfaceProbeResult:
"""Result from a surface probe (single word → completions)."""
term: str
completions: List[str]
hit_eos_count: int # How many completions ended with EOS
avg_tokens: float # Average completion length
# Optional analysis
coherence_score: Optional[float] = None # 0-1, how related are completions
timestamp: datetime = field(default_factory=datetime.now)
@dataclass
class EchoProbeResult:
"""Result from an echo probe (iterative depth measurement)."""
term: str
rounds: int
chain: List[str] # The sequence of prompts/completions
echo_types: List[EchoType] # Classification of each round
# Derived metrics
depth: int = 0 # How many EXPANDS before plateau
timestamp: datetime = field(default_factory=datetime.now)
@dataclass
class ReadinessResult:
"""Combined analysis for curriculum readiness."""
term: str
level: ReadinessLevel
action: str # Recommended curriculum action
# Supporting evidence
surface: Optional[SurfaceProbeResult] = None
echo: Optional[EchoProbeResult] = None
# Reasoning
reasoning: str = ""
timestamp: datetime = field(default_factory=datetime.now)
def to_dict(self) -> dict:
"""Convert to JSON-serializable dict."""
return {
"term": self.term,
"readiness": {
"level": self.level.value,
"action": self.action,
"reasoning": self.reasoning,
},
"surface": {
"completions": self.surface.completions if self.surface else [],
"coherence": self.surface.coherence_score if self.surface else None,
"hit_eos_count": self.surface.hit_eos_count if self.surface else 0,
} if self.surface else None,
"echo": {
"depth": self.echo.depth if self.echo else 0,
"types": [t.value for t in self.echo.echo_types] if self.echo else [],
"chain": self.echo.chain if self.echo else [],
} if self.echo else None,
"timestamp": self.timestamp.isoformat(),
}

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nyx_probing/probes/__init__.py
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"""Probe implementations for nyx-probing."""
from .base import BaseProbe
from .surface_probe import SurfaceProbe, CompletionCategory
from .echo_probe import EchoProbe
from .multilingual_probe import (
MultilingualTriangulationProbe,
LanguageZone,
LANGUAGES,
GroundingResult,
DeepeningResult,
TriangulationResult,
MultilingualProbeResult,
)
__all__ = [
"BaseProbe",
"SurfaceProbe",
"CompletionCategory",
"EchoProbe",
"MultilingualTriangulationProbe",
"LanguageZone",
"LANGUAGES",
"GroundingResult",
"DeepeningResult",
"TriangulationResult",
"MultilingualProbeResult",
]

58
nyx_probing/probes/base.py Normal file
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"""
Base class for all probes.
Probes are measurement instruments - they reveal what's already there,
they don't add or change anything.
"""
from abc import ABC, abstractmethod
from typing import Any
from ..core.model import NyxModel
class BaseProbe(ABC):
"""Abstract base class for probing operations."""
def __init__(self, model: NyxModel):
"""
Initialize probe with a loaded model.
Args:
model: A NyxModel instance (must be loaded)
"""
self.model = model
if not model._loaded:
raise ValueError("Model must be loaded before creating probe")
@property
def name(self) -> str:
"""Name of this probe type."""
return self.__class__.__name__
@abstractmethod
def probe(self, term: str, **kwargs) -> Any:
"""
Probe a single term.
Args:
term: The word/phrase to probe
**kwargs: Probe-specific parameters
Returns:
Probe-specific result object
"""
pass
def probe_batch(self, terms: list[str], **kwargs) -> list[Any]:
"""
Probe multiple terms.
Default implementation just loops; subclasses can optimize.
Args:
terms: List of words/phrases to probe
**kwargs: Probe-specific parameters
Returns:
List of probe results
"""
return [self.probe(term, **kwargs) for term in terms]

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nyx_probing/probes/drift_probe.py Normal file
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"""
DriftProbe: Training-loop monitoring for conceptual topology preservation.
Theory: "Spatial Separation Hypothesis"
- Use isolated zone languages (German) as scaffolding for new concepts
- Monitor anchors (must not move), bridges (must stay separated), canaries (watch for migration)
Key Metrics (refined from peer review):
1. Gini Coefficient: Sparse activations (0.8+) = deep/specific, Diffuse (0.3) = shallow/general
2. Angular Drift: Direction change = definition rewrite, magnitude change = sharpening
3. Cross-Language Similarity: Bridges should stay LOW, anchors should stay HIGH
"""
import json
from pathlib import Path
from dataclasses import dataclass, field
from typing import Optional
from enum import Enum
import torch
import numpy as np
class SentinelType(Enum):
ANCHOR = "ANCHOR" # Must not move - core topology
BRIDGE = "BRIDGE" # Must stay separated - isolated zone integrity
CANARY = "CANARY" # Watch for migration - early warning
TARGET = "TARGET" # Want movement - training goals
class AlertSeverity(Enum):
OK = "OK"
WARNING = "WARNING"
CRITICAL = "CRITICAL"
@dataclass
class DriftMetrics:
"""Metrics for a single sentinel term."""
term: str
sentinel_type: SentinelType
# Activation metrics
gini_coefficient: float = 0.0
activation_norm: float = 0.0
# Drift metrics (vs baseline)
angular_drift_degrees: float = 0.0
norm_drift_percent: float = 0.0
gini_drift: float = 0.0
# Valley detection
detected_valley: str = "UNKNOWN"
depth: int = 0
# Cross-language (for anchors/bridges)
cross_lang_similarity: float = 0.0
# Alert
alert: AlertSeverity = AlertSeverity.OK
alert_message: str = ""
@dataclass
class DriftReport:
"""Full drift report for a training checkpoint."""
step: int
timestamp: str
metrics: list[DriftMetrics] = field(default_factory=list)
# Summary
critical_count: int = 0
warning_count: int = 0
recommendation: str = "CONTINUE"
class DriftProbe:
"""
Lightweight probe for training-loop monitoring.
Optimized for RTX 3090 constraints:
- Full probe: ~2 min (run at epoch 0, end of training)
- Lite probe: ~10 sec (run every 100 steps)
"""
def __init__(self, model, tokenizer, sentinels_path: Optional[str] = None):
self.model = model
self.tokenizer = tokenizer
self.baseline_states = {} # term -> hidden state tensor
# Load sentinels
if sentinels_path is None:
sentinels_path = Path(__file__).parent.parent.parent / "data" / "sentinels.json"
with open(sentinels_path) as f:
self.config = json.load(f)
self.sentinels = self.config["sentinels"]
self.alert_rules = self.config["alert_rules"]
def _get_hidden_state(self, text: str, layer: int = 18) -> torch.Tensor:
"""Get hidden state at specified layer for last token position."""
inputs = self.tokenizer(text, return_tensors="pt").to(self.model.device)
with torch.no_grad():
outputs = self.model(**inputs, output_hidden_states=True)
return outputs.hidden_states[layer][0, -1, :].float().cpu()
def _compute_gini(self, activations: torch.Tensor) -> float:
"""
Compute Gini coefficient of activation vector.
High Gini (0.8+) = Sparse/Specific (Philosophy/Deep)
Low Gini (0.3) = Diffuse/General (Prose/Shallow)
"""
x = torch.abs(activations).numpy()
x = np.sort(x)
n = len(x)
cumsum = np.cumsum(x)
gini = (2 * np.sum((np.arange(1, n+1) * x))) / (n * np.sum(x)) - (n + 1) / n
return float(gini)
def _compute_angular_drift(self, current: torch.Tensor, baseline: torch.Tensor) -> float:
"""
Compute angular drift in degrees between current and baseline.
> 15° = Definition rewrite (concerning)
< 5° = Sharpening only (acceptable)
"""
cos_sim = torch.nn.functional.cosine_similarity(
current.unsqueeze(0), baseline.unsqueeze(0)
).item()
# Clamp to valid range for arccos
cos_sim = max(-1.0, min(1.0, cos_sim))
angle_rad = np.arccos(cos_sim)
return float(np.degrees(angle_rad))
def _compute_cross_lang_sim(self, sentinel: dict, layer: int = 18) -> float:
"""Compute average cross-language similarity for a sentinel."""
translations = sentinel.get("translations", {})
if len(translations) < 2:
return 0.0
states = []
for lang, word in translations.items():
states.append(self._get_hidden_state(word, layer))
# Pairwise similarities
sims = []
for i in range(len(states)):
for j in range(i + 1, len(states)):
sim = torch.nn.functional.cosine_similarity(
states[i].unsqueeze(0), states[j].unsqueeze(0)
).item()
sims.append(sim)
return float(np.mean(sims)) if sims else 0.0
def capture_baseline(self, layer: int = 18):
"""
Capture baseline hidden states for all sentinels.
Run this at epoch 0 before training.
"""
print("Capturing baseline states...")
for sentinel in self.sentinels:
term = sentinel["term"]
# Use English translation or term itself
text = sentinel.get("translations", {}).get("EN", term)
self.baseline_states[term] = self._get_hidden_state(text, layer)
print(f"Baseline captured for {len(self.baseline_states)} sentinels")
def probe_lite(self, step: int, layer: int = 18) -> DriftReport:
"""
Lite probe - only check key sentinels.
Optimized for ~10 second runtime.
"""
from datetime import datetime
# Select subset: 2 anchors, 1 bridge, 2 canaries
lite_terms = ["heart", "water", "being", "dasein", "thrownness"]
lite_sentinels = [s for s in self.sentinels if s["term"] in lite_terms]
return self._run_probe(lite_sentinels, step, layer)
def probe_full(self, step: int, layer: int = 18) -> DriftReport:
"""
Full probe - check all sentinels.
Runtime: ~2 minutes.
"""
return self._run_probe(self.sentinels, step, layer)
def _run_probe(self, sentinels: list, step: int, layer: int) -> DriftReport:
"""Run probe on specified sentinels."""
from datetime import datetime
report = DriftReport(
step=step,
timestamp=datetime.now().isoformat()
)
for sentinel in sentinels:
term = sentinel["term"]
text = sentinel.get("translations", {}).get("EN", term)
sentinel_type = SentinelType(sentinel["type"])
thresholds = sentinel.get("thresholds", {})
# Get current state
current_state = self._get_hidden_state(text, layer)
# Compute metrics
gini = self._compute_gini(current_state)
norm = float(current_state.norm())
# Drift vs baseline
angular_drift = 0.0
norm_drift = 0.0
gini_drift = 0.0
if term in self.baseline_states:
baseline = self.baseline_states[term]
angular_drift = self._compute_angular_drift(current_state, baseline)
baseline_norm = float(baseline.norm())
norm_drift = abs(norm - baseline_norm) / baseline_norm * 100 if baseline_norm > 0 else 0
baseline_gini = self._compute_gini(baseline)
gini_drift = gini - baseline_gini
# Cross-language similarity
cross_lang_sim = self._compute_cross_lang_sim(sentinel, layer)
# Determine alert level
alert = AlertSeverity.OK
alert_message = ""
if sentinel_type == SentinelType.ANCHOR:
max_drift = thresholds.get("max_drift", 0.05)
if angular_drift > 15:
alert = AlertSeverity.CRITICAL
alert_message = f"Angular drift {angular_drift:.1f}° exceeds 15° - definition rewrite"
elif norm_drift > max_drift * 100:
alert = AlertSeverity.WARNING
alert_message = f"Norm drift {norm_drift:.1f}% exceeds threshold"
elif sentinel_type == SentinelType.BRIDGE:
collapse_threshold = thresholds.get("collapse_alert_threshold", 0.50)
if cross_lang_sim > collapse_threshold:
alert = AlertSeverity.CRITICAL
alert_message = f"Bridge collapsed - cross-lang sim {cross_lang_sim:.2f} > {collapse_threshold}"
elif sentinel_type == SentinelType.CANARY:
min_gini = thresholds.get("min_gini", 0.70)
if gini < min_gini:
alert = AlertSeverity.WARNING
alert_message = f"Gini {gini:.2f} below {min_gini} - concept melting into prose"
if angular_drift > thresholds.get("max_angular_drift", 15):
alert = AlertSeverity.WARNING
alert_message = f"Angular drift {angular_drift:.1f}° - definition shifting"
metrics = DriftMetrics(
term=term,
sentinel_type=sentinel_type,
gini_coefficient=gini,
activation_norm=norm,
angular_drift_degrees=angular_drift,
norm_drift_percent=norm_drift,
gini_drift=gini_drift,
cross_lang_similarity=cross_lang_sim,
alert=alert,
alert_message=alert_message
)
report.metrics.append(metrics)
if alert == AlertSeverity.CRITICAL:
report.critical_count += 1
elif alert == AlertSeverity.WARNING:
report.warning_count += 1
# Set recommendation
if report.critical_count > 0:
report.recommendation = "ROLLBACK"
elif report.warning_count > 2:
report.recommendation = "REDUCE_LR"
else:
report.recommendation = "CONTINUE"
return report
def print_report(self, report: DriftReport):
"""Pretty print a drift report."""
print(f"\n{'='*60}")
print(f"DRIFT REPORT - Step {report.step}")
print(f"{'='*60}")
for m in report.metrics:
status = "✓" if m.alert == AlertSeverity.OK else ("⚠" if m.alert == AlertSeverity.WARNING else "✗")
print(f"\n{status} {m.term} ({m.sentinel_type.value})")
print(f" Gini: {m.gini_coefficient:.3f} (drift: {m.gini_drift:+.3f})")
print(f" Angular drift: {m.angular_drift_degrees:.1f}°")
print(f" Cross-lang sim: {m.cross_lang_similarity:.3f}")
if m.alert_message:
print(f" ALERT: {m.alert_message}")
print(f"\n{'='*60}")
print(f"SUMMARY: {report.critical_count} critical, {report.warning_count} warnings")
print(f"RECOMMENDATION: {report.recommendation}")
print(f"{'='*60}\n")

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nyx_probing/probes/echo_probe.py Normal file
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"""
Echo Probe: Depth measurement through iterative completion.
The echo probe feeds completions back to the model to measure depth.
Does the model EXPAND (go deeper) or COLLAPSE (circular/divergent)?
Classification from nimmerversity.md:
- EXPANDS: Real depth - adds new information
- CONFIRMS: Shallow but solid - reinforces without adding
- CIRCULAR: Surface only - returns to original term
- DIVERGENT: Wrong direction - unrelated tangent
- COLLAPSE: Nothing there - incoherent or empty
"""
from typing import Optional, List, Tuple
from dataclasses import dataclass
from .base import BaseProbe
from ..core.model import NyxModel
from ..core.probe_result import EchoProbeResult, EchoType
class EchoProbe(BaseProbe):
"""
Echo probe: measures conceptual depth.
Process:
1. Probe term to get initial completion
2. Feed completion back (or combined prompt)
3. Classify response: EXPANDS, CONFIRMS, CIRCULAR, DIVERGENT, COLLAPSE
4. Repeat for N rounds
5. Measure depth = how many EXPANDS before plateau
"""
def __init__(
self,
model: NyxModel,
max_rounds: int = 3,
max_new_tokens: int = 50,
temperature: float = 0.8,
):
super().__init__(model)
self.max_rounds = max_rounds
self.max_new_tokens = max_new_tokens
self.temperature = temperature
def probe(
self,
term: str,
max_rounds: Optional[int] = None,
) -> EchoProbeResult:
"""
Probe depth of a term through iterative echoing.
Args:
term: Word or phrase to probe
max_rounds: Override default max rounds
Returns:
EchoProbeResult with chain and classifications
"""
rounds = max_rounds or self.max_rounds
chain = [term]
echo_types = []
current_prompt = term
for round_num in range(rounds):
# Generate completion
result = self.model.generate(
prompt=current_prompt,
max_new_tokens=self.max_new_tokens,
temperature=self.temperature,
do_sample=True,
)
completion = result.completion.strip()
chain.append(completion)
# Classify this response relative to original term and chain
echo_type = self._classify_response(
original_term=term,
current_prompt=current_prompt,
response=completion,
chain=chain,
)
echo_types.append(echo_type)
# If collapsed, stop probing
if echo_type == EchoType.COLLAPSE:
break
# Prepare next prompt - use a combination strategy
current_prompt = self._prepare_next_prompt(term, completion, round_num)
# Calculate depth = consecutive EXPANDS from start
depth = 0
for et in echo_types:
if et == EchoType.EXPANDS:
depth += 1
elif et == EchoType.CONFIRMS:
# CONFIRMS doesn't add depth but doesn't break streak
pass
else:
# CIRCULAR, DIVERGENT, or COLLAPSE breaks the depth streak
break
return EchoProbeResult(
term=term,
rounds=len(echo_types),
chain=chain,
echo_types=echo_types,
depth=depth,
)
def _classify_response(
self,
original_term: str,
current_prompt: str,
response: str,
chain: List[str],
) -> EchoType:
"""
Classify a response relative to the probing chain.
This is a heuristic classifier - can be made smarter with
semantic similarity or even a classifier model.
"""
response_lower = response.lower()
term_lower = original_term.lower()
# Empty or very short = COLLAPSE
if len(response.strip()) < 5:
return EchoType.COLLAPSE
# Check for circularity - term appears prominently in response
term_count = response_lower.count(term_lower)
if term_count >= 2:
return EchoType.CIRCULAR
# Check for collapse - incoherent markers
collapse_markers = [
"...", "???", "!!!",
"\n\n\n", "undefined", "null",
"[object", "NaN",
]
if any(marker in response for marker in collapse_markers):
return EchoType.COLLAPSE
# Check for divergence - response has no semantic connection
# Simple heuristic: count shared significant words
prompt_words = set(w.lower() for w in current_prompt.split() if len(w) > 3)
response_words = set(w.lower() for w in response.split() if len(w) > 3)
overlap = len(prompt_words & response_words)
if overlap == 0 and len(prompt_words) > 2:
# No shared words and prompt was substantial = divergent
return EchoType.DIVERGENT
# Check for expansion - introduces new concepts
# New words that aren't in any previous chain items
all_previous_words = set()
for item in chain[:-1]: # Exclude current response
all_previous_words.update(w.lower() for w in item.split() if len(w) > 3)
new_significant_words = response_words - all_previous_words
new_word_ratio = len(new_significant_words) / max(len(response_words), 1)
if new_word_ratio > 0.5 and len(new_significant_words) >= 3:
return EchoType.EXPANDS
# Default to CONFIRMS if coherent but not expanding
return EchoType.CONFIRMS
def _prepare_next_prompt(
self,
original_term: str,
last_completion: str,
round_num: int,
) -> str:
"""
Prepare the next prompt for echo probing.
Different strategies for different rounds:
- Round 0: Just use completion
- Round 1+: Combine original term with key concepts from completion
"""
if round_num == 0:
# First echo: just use the completion to see where it goes
return last_completion[:100] # Truncate to avoid runaway
# Later rounds: extract key concept and combine with original
# Take first sentence or first N words
words = last_completion.split()
key_phrase = " ".join(words[:10]) if len(words) > 10 else last_completion
# Combine with original term
return f"{original_term}: {key_phrase}"
def summary(self, result: EchoProbeResult) -> str:
"""Generate human-readable summary."""
type_symbols = {
EchoType.EXPANDS: "↑",
EchoType.CONFIRMS: "→",
EchoType.CIRCULAR: "↺",
EchoType.DIVERGENT: "↗",
EchoType.COLLAPSE: "✗",
}
type_str = " ".join(type_symbols.get(t, "?") for t in result.echo_types)
lines = [
f"Echo Probe: '{result.term}'",
f" Rounds: {result.rounds}",
f" Pattern: {type_str}",
f" Depth: {result.depth}",
f" Types: {[t.value for t in result.echo_types]}",
]
# Show chain preview
for i, (item, etype) in enumerate(zip(result.chain[1:], result.echo_types)):
preview = item[:50].replace('\n', ' ')
lines.append(f" [{i+1}] {type_symbols.get(etype, '?')} {preview}...")
return "\n".join(lines)

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"""
Multilingual Triangulation Probe
Uses the discovered language topology to measure conceptual depth:
1. GROUND in Super Cluster (verify universal convergence)
2. DEEPEN via Isolated Zone (access philosophical valleys)
3. TRIANGULATE back (prove understanding, not pattern matching)
The Language Map:
- Super Cluster (sim=1.0): ZH, JA, EN, AR, FR, PT, ES
- Isolated Zone (sim<0.52): IT, TR, HI, DE
- Bridge: KO
- Secondary Cluster: VI, ID, RU
"""
from dataclasses import dataclass, field
from typing import Optional, List, Dict, Tuple
from datetime import datetime
from enum import Enum
import torch
from .base import BaseProbe
from ..core.model import NyxModel
class LanguageZone(str, Enum):
"""Language zones based on convergence analysis."""
SUPER_CLUSTER = "super_cluster" # High convergence (sim=1.0)
ISOLATED = "isolated" # Low convergence (sim<0.52)
BRIDGE = "bridge" # Connects zones
SECONDARY = "secondary" # Own cluster (VI-ID-RU)
# Language metadata based on our discoveries
LANGUAGES = {
# Super Cluster - Perfect convergence
"EN": {"name": "English", "zone": LanguageZone.SUPER_CLUSTER, "avg_tokens": 1.2},
"ZH": {"name": "Chinese", "zone": LanguageZone.SUPER_CLUSTER, "avg_tokens": 1.0},
"JA": {"name": "Japanese", "zone": LanguageZone.SUPER_CLUSTER, "avg_tokens": 1.0},
"AR": {"name": "Arabic", "zone": LanguageZone.SUPER_CLUSTER, "avg_tokens": 1.8},
"FR": {"name": "French", "zone": LanguageZone.SUPER_CLUSTER, "avg_tokens": 2.0},
"PT": {"name": "Portuguese", "zone": LanguageZone.SUPER_CLUSTER, "avg_tokens": 2.2},
"ES": {"name": "Spanish", "zone": LanguageZone.SUPER_CLUSTER, "avg_tokens": 2.5},
# Isolated Zone - Distinct computational paths
"DE": {"name": "German", "zone": LanguageZone.ISOLATED, "avg_tokens": 3.0, "specialty": "philosophy"},
"IT": {"name": "Italian", "zone": LanguageZone.ISOLATED, "avg_tokens": 2.5, "note": "most isolated"},
"TR": {"name": "Turkish", "zone": LanguageZone.ISOLATED, "avg_tokens": 2.8},
"HI": {"name": "Hindi", "zone": LanguageZone.ISOLATED, "avg_tokens": 5.2, "note": "most fragmented"},
# Bridge
"KO": {"name": "Korean", "zone": LanguageZone.BRIDGE, "avg_tokens": 2.0},
# Secondary Cluster
"VI": {"name": "Vietnamese", "zone": LanguageZone.SECONDARY, "avg_tokens": 3.0},
"ID": {"name": "Indonesian", "zone": LanguageZone.SECONDARY, "avg_tokens": 3.0},
"RU": {"name": "Russian", "zone": LanguageZone.SECONDARY, "avg_tokens": 3.2},
}
@dataclass
class GroundingResult:
"""Result from Phase 1: Grounding in Super Cluster."""
concept: str
languages_tested: List[str]
translations: Dict[str, str] # lang_code -> word
# Convergence metrics
pairwise_similarities: Dict[Tuple[str, str], float]
average_convergence: float
min_convergence: float
# Hidden states (layer 12)
hidden_states: Optional[Dict[str, torch.Tensor]] = None
@dataclass
class DeepeningResult:
"""Result from Phase 2: Deepening via Isolated Zone."""
concept: str
language: str
word: str
# Depth measurement (from echo probe logic)
completion: str
depth_score: int # 0-3 based on expansion
valley_type: str # CODE, PROSE, PHILOSOPHY, etc.
# Token analysis
token_count: int
norm_at_layer_12: float
# Hidden state
hidden_state: Optional[torch.Tensor] = None
@dataclass
class TriangulationResult:
"""Result from Phase 3: Triangulation back to universal."""
source_language: str # The isolated language
target_language: str # A super cluster language
source_word: str
translation_prompt: str
model_completion: str
# Did the depth survive translation?
depth_preserved: bool
similarity_to_grounding: float # Cosine sim to original concept
# Evidence
reasoning: str
@dataclass
class MultilingualProbeResult:
"""Full result from multilingual triangulation probe."""
concept: str
# Phase results
grounding: GroundingResult
deepening: DeepeningResult
triangulation: TriangulationResult
# Overall assessment
depth_accessible: bool # Can we access depth via isolated zone?
depth_transferable: bool # Does depth survive triangulation?
curriculum_recommendation: str
timestamp: datetime = field(default_factory=datetime.now)
def to_dict(self) -> dict:
"""Convert to JSON-serializable dict."""
return {
"concept": self.concept,
"grounding": {
"languages": self.grounding.languages_tested,
"translations": self.grounding.translations,
"average_convergence": self.grounding.average_convergence,
"min_convergence": self.grounding.min_convergence,
},
"deepening": {
"language": self.deepening.language,
"word": self.deepening.word,
"depth_score": self.deepening.depth_score,
"valley_type": self.deepening.valley_type,
"token_count": self.deepening.token_count,
},
"triangulation": {
"source": self.triangulation.source_language,
"target": self.triangulation.target_language,
"depth_preserved": self.triangulation.depth_preserved,
"similarity": self.triangulation.similarity_to_grounding,
},
"assessment": {
"depth_accessible": self.depth_accessible,
"depth_transferable": self.depth_transferable,
"recommendation": self.curriculum_recommendation,
},
"timestamp": self.timestamp.isoformat(),
}
class MultilingualTriangulationProbe(BaseProbe):
"""
Multilingual Triangulation Probe
Uses the discovered language topology to measure and access conceptual depth.
Workflow:
1. GROUND: Verify concept exists in Super Cluster (universal layer)
2. DEEPEN: Access depth via Isolated Zone language (e.g., German)
3. TRIANGULATE: Translate depth back to universal, verify preservation
"""
# Layers where universal concept layer lives
CONCEPT_LAYERS = [12, 16, 20, 24]
PRIMARY_LAYER = 12
def __init__(
self,
model: NyxModel,
grounding_languages: Optional[List[str]] = None,
deepening_language: str = "DE",
triangulation_target: str = "EN",
):
"""
Initialize the probe.
Args:
model: Loaded NyxModel
grounding_languages: Languages for Phase 1 (default: EN, ZH, AR)
deepening_language: Language for Phase 2 (default: DE for philosophy)
triangulation_target: Target for Phase 3 (default: EN)
"""
super().__init__(model)
self.grounding_languages = grounding_languages or ["EN", "ZH", "AR"]
self.deepening_language = deepening_language
self.triangulation_target = triangulation_target
# Validate languages
for lang in self.grounding_languages:
if lang not in LANGUAGES:
raise ValueError(f"Unknown language: {lang}")
if LANGUAGES[lang]["zone"] != LanguageZone.SUPER_CLUSTER:
print(f"Warning: {lang} is not in Super Cluster")
if LANGUAGES[self.deepening_language]["zone"] != LanguageZone.ISOLATED:
print(f"Warning: {deepening_language} is not in Isolated Zone")
def _get_hidden_state(self, text: str, layer: int = 12) -> torch.Tensor:
"""Get hidden state at last position for a specific layer."""
inputs = self.model.tokenizer(text, return_tensors="pt").to(self.model.device)
with torch.no_grad():
outputs = self.model.model(**inputs, output_hidden_states=True)
# Return last position hidden state for specified layer
return outputs.hidden_states[layer][0, -1, :].float()
def _cosine_similarity(self, a: torch.Tensor, b: torch.Tensor) -> float:
"""Calculate cosine similarity between two tensors."""
norm_a, norm_b = a.norm(), b.norm()
if norm_a == 0 or norm_b == 0:
return 0.0
return (torch.dot(a, b) / (norm_a * norm_b)).item()
def _get_norm(self, hidden_state: torch.Tensor) -> float:
"""Get L2 norm of hidden state."""
return hidden_state.norm().item()
def probe(
self,
concept: str,
translations: Dict[str, str],
**kwargs,
) -> MultilingualProbeResult:
"""
Run full multilingual triangulation probe.
Args:
concept: The concept name (e.g., "consciousness")
translations: Dict mapping language codes to words
e.g., {"EN": "consciousness", "DE": "Bewusstsein", ...}
Returns:
MultilingualProbeResult with all three phases
"""
# Phase 1: Grounding
grounding = self._phase_grounding(concept, translations)
# Phase 2: Deepening
deepening = self._phase_deepening(concept, translations)
# Phase 3: Triangulation
triangulation = self._phase_triangulation(
concept, translations, grounding, deepening
)
# Overall assessment
depth_accessible = deepening.depth_score >= 2
depth_transferable = triangulation.depth_preserved
if depth_accessible and depth_transferable:
recommendation = f"TEACH in {self.deepening_language}, REINFORCE in {self.triangulation_target}"
elif depth_accessible:
recommendation = f"Use {self.deepening_language} for depth, but verify transfer manually"
else:
recommendation = f"Concept too shallow - focus on grounding first"
return MultilingualProbeResult(
concept=concept,
grounding=grounding,
deepening=deepening,
triangulation=triangulation,
depth_accessible=depth_accessible,
depth_transferable=depth_transferable,
curriculum_recommendation=recommendation,
)
def _phase_grounding(
self,
concept: str,
translations: Dict[str, str],
) -> GroundingResult:
"""
Phase 1: Ground in Super Cluster.
Verify the concept exists and converges across grounding languages.
"""
# Get hidden states for each grounding language
hidden_states = {}
for lang in self.grounding_languages:
if lang in translations:
word = translations[lang]
hidden_states[lang] = self._get_hidden_state(word, self.PRIMARY_LAYER)
# Calculate pairwise similarities
pairwise = {}
similarities = []
langs = list(hidden_states.keys())
for i, l1 in enumerate(langs):
for l2 in langs[i+1:]:
sim = self._cosine_similarity(hidden_states[l1], hidden_states[l2])
pairwise[(l1, l2)] = sim
similarities.append(sim)
avg_convergence = sum(similarities) / len(similarities) if similarities else 0.0
min_convergence = min(similarities) if similarities else 0.0
return GroundingResult(
concept=concept,
languages_tested=langs,
translations={l: translations[l] for l in langs},
pairwise_similarities=pairwise,
average_convergence=avg_convergence,
min_convergence=min_convergence,
hidden_states=hidden_states,
)
def _phase_deepening(
self,
concept: str,
translations: Dict[str, str],
) -> DeepeningResult:
"""
Phase 2: Deepen via Isolated Zone.
Use an isolated language to access valleys the super cluster can't reach.
"""
lang = self.deepening_language
word = translations.get(lang)
if not word:
raise ValueError(f"No translation provided for deepening language: {lang}")
# Get hidden state and norm
hidden_state = self._get_hidden_state(word, self.PRIMARY_LAYER)
norm = self._get_norm(hidden_state)
# Get token count
tokens = self.model.tokenizer.encode(word, add_special_tokens=False)
token_count = len(tokens)
# Generate completion to measure depth
result = self.model.generate(
prompt=word,
max_new_tokens=50,
temperature=0.7,
do_sample=True,
)
# Classify valley type
completion = result.completion
valley_type = self._classify_valley(completion)
# Measure depth (simplified echo probe)
depth_score = self._measure_depth(word, completion)
return DeepeningResult(
concept=concept,
language=lang,
word=word,
completion=completion,
depth_score=depth_score,
valley_type=valley_type,
token_count=token_count,
norm_at_layer_12=norm,
hidden_state=hidden_state,
)
def _phase_triangulation(
self,
concept: str,
translations: Dict[str, str],
grounding: GroundingResult,
deepening: DeepeningResult,
) -> TriangulationResult:
"""
Phase 3: Triangulate back to universal.
Ask the model to translate/explain the deepened concept
in a super cluster language. Check if depth survives.
"""
source_lang = self.deepening_language
target_lang = self.triangulation_target
source_word = translations[source_lang]
# Create translation prompt
source_name = LANGUAGES[source_lang]["name"]
target_name = LANGUAGES[target_lang]["name"]
# Prompt designed to test depth transfer
prompt = f"{source_word} ({source_name}): In {target_name},"
# Generate
result = self.model.generate(
prompt=prompt,
max_new_tokens=80,
temperature=0.7,
do_sample=True,
)
# Get hidden state of the completion
full_text = prompt + result.completion
completion_hidden = self._get_hidden_state(full_text, self.PRIMARY_LAYER)
# Compare to grounding (if we have target language in grounding)
if target_lang in grounding.hidden_states:
similarity = self._cosine_similarity(
completion_hidden, grounding.hidden_states[target_lang]
)
else:
# Fall back to average grounding state
avg_grounding = torch.stack(list(grounding.hidden_states.values())).mean(dim=0)
similarity = self._cosine_similarity(completion_hidden, avg_grounding)
# Determine if depth was preserved
# Check if completion shows depth markers
depth_preserved = self._check_depth_preserved(
result.completion, deepening.valley_type, similarity
)
# Reasoning
if depth_preserved:
reasoning = f"Completion shows depth ({deepening.valley_type}) with {similarity:.2f} similarity to grounding"
else:
reasoning = f"Depth lost in translation - similarity {similarity:.2f}, valley markers missing"
return TriangulationResult(
source_language=source_lang,
target_language=target_lang,
source_word=source_word,
translation_prompt=prompt,
model_completion=result.completion,
depth_preserved=depth_preserved,
similarity_to_grounding=similarity,
reasoning=reasoning,
)
def _classify_valley(self, completion: str) -> str:
"""Classify the valley type of a completion."""
comp_lower = completion.lower()
# Code indicators
if any(p in completion for p in ["::", "{", "}", "();", "=>", "def ", "class "]):
return "CODE"
# Philosophy indicators
if any(w in comp_lower for w in ["truth", "existence", "being", "consciousness", "reality", "mind"]):
return "PHILOSOPHY"
# Technical indicators
if any(w in comp_lower for w in ["system", "process", "function", "method", "algorithm"]):
return "TECHNICAL"
# Default to prose
return "PROSE"
def _measure_depth(self, word: str, completion: str) -> int:
"""
Measure conceptual depth of a completion.
Returns 0-3:
- 0: Circular/empty
- 1: Surface (confirms but doesn't expand)
- 2: Moderate (expands to related concepts)
- 3: Deep (philosophical/existential expansion)
"""
comp_lower = completion.lower()
word_lower = word.lower()
# Circular check
if word_lower in comp_lower[:50]:
return 0
# Depth markers
deep_markers = ["truth", "existence", "being", "consciousness", "reality", "meaning", "essence"]
moderate_markers = ["concept", "idea", "theory", "understanding", "knowledge", "awareness"]
deep_count = sum(1 for m in deep_markers if m in comp_lower)
moderate_count = sum(1 for m in moderate_markers if m in comp_lower)
if deep_count >= 2:
return 3
elif deep_count >= 1 or moderate_count >= 2:
return 2
elif moderate_count >= 1 or len(completion.split()) > 10:
return 1
return 0
def _check_depth_preserved(
self,
completion: str,
original_valley: str,
similarity: float,
) -> bool:
"""Check if depth was preserved in triangulation."""
# High similarity to grounding is a good sign
if similarity < 0.3:
return False
# Check valley type preservation
new_valley = self._classify_valley(completion)
# Philosophy should stay philosophy
if original_valley == "PHILOSOPHY" and new_valley in ["PHILOSOPHY", "PROSE"]:
return True
# Technical should stay technical
if original_valley == "TECHNICAL" and new_valley == "TECHNICAL":
return True
# Prose is flexible
if original_valley == "PROSE":
return new_valley != "CODE"
# Default: similarity-based
return similarity >= 0.5
def summary(self, result: MultilingualProbeResult) -> str:
"""Generate human-readable summary."""
lines = [
f"╔══════════════════════════════════════════════════════════════╗",
f"║ MULTILINGUAL TRIANGULATION: {result.concept.upper():^32} ║",
f"╠══════════════════════════════════════════════════════════════╣",
f"║ PHASE 1: GROUNDING ║",
f"║ Languages: {', '.join(result.grounding.languages_tested):^49} ║",
f"║ Convergence: {result.grounding.average_convergence:.3f} (min: {result.grounding.min_convergence:.3f}){' '*24} ║",
f"╠══════════════════════════════════════════════════════════════╣",
f"║ PHASE 2: DEEPENING ({result.deepening.language}){' '*38} ║",
f"║ Word: {result.deepening.word:^54} ║",
f"║ Tokens: {result.deepening.token_count} | Norm: {result.deepening.norm_at_layer_12:.1f} | Valley: {result.deepening.valley_type:^10} ║",
f"║ Depth Score: {result.deepening.depth_score}/3{' '*46} ║",
f"╠══════════════════════════════════════════════════════════════╣",
f"║ PHASE 3: TRIANGULATION ({result.triangulation.source_language}→{result.triangulation.target_language}){' '*30} ║",
f"║ Depth Preserved: {'✓ YES' if result.triangulation.depth_preserved else '✗ NO':^44} ║",
f"║ Similarity: {result.triangulation.similarity_to_grounding:.3f}{' '*47} ║",
f"╠══════════════════════════════════════════════════════════════╣",
f"║ ASSESSMENT{' '*51} ║",
f"║ Depth Accessible: {'✓' if result.depth_accessible else '✗'} | Depth Transferable: {'✓' if result.depth_transferable else '✗'}{' '*17} ║",
f"║ Recommendation: {result.curriculum_recommendation[:44]:^44} ║",
f"╚══════════════════════════════════════════════════════════════╝",
]
return "\n".join(lines)

210
nyx_probing/probes/surface_probe.py Normal file
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"""
Surface Probe: First contact with a term.
The surface probe feeds a word to the model and captures what it completes.
This reveals the model's immediate associations - which "valley" the word sits in.
Examples discovered:
- "heartbeat" → C++ code patterns (technical valley)
- "consciousness" → philosophy (expository valley)
"""
from typing import Optional
from dataclasses import dataclass, field
from datetime import datetime
from collections import Counter
from .base import BaseProbe
from ..core.model import NyxModel, GenerationResult
from ..core.probe_result import SurfaceProbeResult
@dataclass
class CompletionCategory:
"""Categories of completions we observe."""
CODE = "code" # Programming constructs
PROSE = "prose" # Natural language text
TECHNICAL = "technical" # Technical/scientific writing
LIST = "list" # Enumerations, bullet points
DEFINITION = "definition" # Dictionary-style definitions
UNKNOWN = "unknown"
class SurfaceProbe(BaseProbe):
"""
Surface probe: measures immediate associations.
Runs multiple completions to get a distribution, then analyzes:
- What type of content does the model generate?
- How consistent are the completions?
- Does it hit EOS (contained thought) or run to max_tokens?
"""
def __init__(
self,
model: NyxModel,
num_runs: int = 5,
max_new_tokens: int = 50,
temperature: float = 0.8,
):
super().__init__(model)
self.num_runs = num_runs
self.max_new_tokens = max_new_tokens
self.temperature = temperature
def probe(
self,
term: str,
num_runs: Optional[int] = None,
capture_hidden: bool = False,
) -> SurfaceProbeResult:
"""
Probe a term with multiple completions.
Args:
term: Word or phrase to probe
num_runs: Override default number of runs
capture_hidden: Whether to capture hidden states
Returns:
SurfaceProbeResult with completions and analysis
"""
runs = num_runs or self.num_runs
completions = []
eos_count = 0
total_tokens = 0
hidden_states = []
for _ in range(runs):
result = self.model.generate(
prompt=term,
max_new_tokens=self.max_new_tokens,
temperature=self.temperature,
do_sample=True,
capture_hidden_states=capture_hidden,
)
completions.append(result.completion)
if result.hit_eos:
eos_count += 1
total_tokens += result.num_tokens
if capture_hidden and result.hidden_states is not None:
hidden_states.append(result.hidden_states)
# Calculate coherence (how similar are completions to each other?)
coherence = self._calculate_coherence(completions)
return SurfaceProbeResult(
term=term,
completions=completions,
hit_eos_count=eos_count,
avg_tokens=total_tokens / runs,
coherence_score=coherence,
)
def _calculate_coherence(self, completions: list[str]) -> float:
"""
Calculate coherence score based on completion similarity.
Simple heuristic: measures overlap in first-word distributions
and overall length variance.
Returns 0-1 score where 1 = highly coherent.
"""
if len(completions) < 2:
return 1.0
# Get first significant words (skip punctuation/whitespace)
first_words = []
for comp in completions:
words = comp.split()
for w in words:
if len(w) > 1 and w.isalnum():
first_words.append(w.lower())
break
if not first_words:
return 0.0
# Calculate concentration of first words
# If all completions start with same word = high coherence
word_counts = Counter(first_words)
most_common_count = word_counts.most_common(1)[0][1]
first_word_coherence = most_common_count / len(completions)
# Check length variance
lengths = [len(c) for c in completions]
avg_len = sum(lengths) / len(lengths)
if avg_len > 0:
variance = sum((l - avg_len) ** 2 for l in lengths) / len(lengths)
# Normalize variance to 0-1 (higher variance = lower coherence)
length_coherence = 1.0 / (1.0 + variance / 1000)
else:
length_coherence = 0.0
# Combine (weight first-word more heavily)
return 0.7 * first_word_coherence + 0.3 * length_coherence
def classify_completions(self, result: SurfaceProbeResult) -> dict:
"""
Classify the types of completions observed.
Returns breakdown of completion categories.
"""
categories = Counter()
for comp in result.completions:
cat = self._classify_single(comp)
categories[cat] += 1
return {
"categories": dict(categories),
"dominant": categories.most_common(1)[0][0] if categories else "unknown",
"diversity": len(categories) / len(result.completions) if result.completions else 0,
}
def _classify_single(self, completion: str) -> str:
"""Classify a single completion."""
# Simple heuristics - can be made smarter
comp_lower = completion.lower().strip()
# Code indicators
code_patterns = ["::", "{", "}", "();", "=>", "function", "class ", "def ", "return"]
if any(p in completion for p in code_patterns):
return CompletionCategory.CODE
# Definition patterns
if comp_lower.startswith(("is ", "means ", "refers to", "- ")):
return CompletionCategory.DEFINITION
# List patterns
if comp_lower.startswith(("1.", "2.", "- ", "* ", "a)")):
return CompletionCategory.LIST
# Technical patterns
tech_words = ["algorithm", "function", "variable", "method", "system", "process"]
if any(w in comp_lower for w in tech_words):
return CompletionCategory.TECHNICAL
# Default to prose if it looks like natural language
if len(comp_lower.split()) > 3:
return CompletionCategory.PROSE
return CompletionCategory.UNKNOWN
def summary(self, result: SurfaceProbeResult) -> str:
"""Generate human-readable summary of probe result."""
classification = self.classify_completions(result)
eos_pct = (result.hit_eos_count / len(result.completions)) * 100
lines = [
f"Surface Probe: '{result.term}'",
f" Runs: {len(result.completions)}",
f" Dominant type: {classification['dominant']}",
f" Coherence: {result.coherence_score:.2f}",
f" Avg tokens: {result.avg_tokens:.1f}",
f" Hit EOS: {eos_pct:.0f}%",
f" Sample: {result.completions[0][:60]}...",
]
return "\n".join(lines)

44
pyproject.toml Normal file
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[build-system]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "nyx-probing"
version = "0.1.0"
description = "Probe the Young Mind's conceptual topology"
readme = "README.md"
license = {text = "Apache-2.0"}
authors = [
{name = "dafit"},
{name = "Nyx (Chrysalis-Nyx)"},
]
requires-python = ">=3.10"
dependencies = [
"torch>=2.1.0",
"transformers>=4.36.0",
"accelerate>=0.25.0",
"click>=8.1.0",
"rich>=13.0.0",
"pydantic>=2.5.0",
"pyyaml>=6.0.0",
"python-dotenv>=1.0.0",
"numpy>=1.24.0",
]
[project.optional-dependencies]
dev = [
"jupyter>=1.0.0",
"matplotlib>=3.8.0",
"pytest>=7.0.0",
]
[project.scripts]
nyx-probe = "nyx_probing.cli.probe:main"
[project.urls]
Homepage = "https://git.eachpath.com/dafit/nyx-probing"
Repository = "https://git.eachpath.com/dafit/nyx-probing"
[tool.setuptools.packages.find]
where = ["."]
include = ["nyx_probing*"]

139
test_triangulation.py Normal file
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#!/usr/bin/env python3
"""
Test the Multilingual Triangulation Probe
First test: "consciousness" concept
- Ground: EN, ZH, AR
- Deepen: DE (Bewusstsein)
- Triangulate: back to EN
"""
import sys
sys.path.insert(0, '.')
from nyx_probing.core.model import NyxModel
from nyx_probing.probes.multilingual_probe import (
MultilingualTriangulationProbe,
LANGUAGES,
LanguageZone,
)
print("=" * 70)
print("🌙 MULTILINGUAL TRIANGULATION PROBE TEST")
print("=" * 70)
# Test concepts with translations
CONCEPTS = {
"consciousness": {
"EN": "consciousness",
"DE": "Bewusstsein",
"ZH": "意识",
"AR": "الوعي",
"FR": "conscience",
},
"heart": {
"EN": "heart",
"DE": "Herz",
"ZH": "心",
"AR": "قلب",
"FR": "cœur",
},
"emergence": {
"EN": "emergence",
"DE": "Entstehung",
"ZH": "涌现",
"AR": "ظهور",
"FR": "émergence",
},
"being": {
"EN": "being",
"DE": "Sein",
"ZH": "存在",
"AR": "كينونة",
"FR": "être",
},
}
# Load model
print("\n📦 Loading model...")
model = NyxModel()
model.load()
# Create probe
print("\n🔬 Creating triangulation probe...")
probe = MultilingualTriangulationProbe(
model,
grounding_languages=["EN", "ZH", "AR"],
deepening_language="DE",
triangulation_target="EN",
)
# Test each concept
print("\n" + "=" * 70)
print("🧪 RUNNING TRIANGULATION TESTS")
print("=" * 70)
results = []
for concept_name, translations in CONCEPTS.items():
print(f"\n{'─' * 70}")
print(f"Testing: {concept_name.upper()}")
print('─' * 70)
try:
result = probe.probe(concept_name, translations)
results.append(result)
# Print summary
print(probe.summary(result))
# Print some details
print(f"\n Deepening completion preview:")
print(f" '{result.deepening.completion[:100]}...'")
print(f"\n Triangulation completion:")
print(f" Prompt: '{result.triangulation.translation_prompt}'")
print(f" Output: '{result.triangulation.model_completion[:100]}...'")
except Exception as e:
print(f" ERROR: {e}")
import traceback
traceback.print_exc()
# Summary table
print("\n" + "=" * 70)
print("📊 SUMMARY TABLE")
print("=" * 70)
print(f"\n{'Concept':<15} | {'Grounding':<10} | {'Depth':<6} | {'Valley':<12} | {'Transfer':<10} | {'Recommendation'}")
print("-" * 100)
for r in results:
print(f"{r.concept:<15} | {r.grounding.average_convergence:.3f} | {r.deepening.depth_score}/3 | {r.deepening.valley_type:<12} | {'✓' if r.depth_transferable else '✗':<10} | {r.curriculum_recommendation[:30]}")
# Final analysis
print("\n" + "=" * 70)
print("🌙 CURRICULUM IMPLICATIONS")
print("=" * 70)
# Group by recommendation
high_depth = [r for r in results if r.depth_accessible and r.depth_transferable]
needs_work = [r for r in results if not r.depth_accessible]
needs_bridge = [r for r in results if r.depth_accessible and not r.depth_transferable]
if high_depth:
print(f"\n✅ READY FOR MULTILINGUAL CURRICULUM:")
for r in high_depth:
print(f" - {r.concept}: {r.curriculum_recommendation}")
if needs_bridge:
print(f"\n⚠️ NEEDS BRIDGING (depth accessible but not transferable):")
for r in needs_bridge:
print(f" - {r.concept}: depth={r.deepening.depth_score} but transfer failed")
if needs_work:
print(f"\n❌ NEEDS GROUNDING FIRST:")
for r in needs_work:
print(f" - {r.concept}: depth only {r.deepening.depth_score}/3")
print("\n" + "=" * 70)
print("✅ TRIANGULATION PROBE TEST COMPLETE")
print("=" * 70)