refactor: hierarchical convergence of documentation (v5.0)

- Create architecture/ and operations/ subdirectories for essential docs
- Archive 10 supporting docs to archive/
- Write fresh Endgame-Vision.md v5.0 (383 lines, down from 2284)
- Add operations/Spark-Protocol.md (condensed boot sequence)
- Integrate December 2025 discoveries (Language is Topology, DriftProbe)
- Update README.md with new structure

New layer structure:
- Layer 0: Temporal Foundation (Heartbeat)
- Layer 1: Cellular Society (Evolution Engine)
- Layer 1.5: Cognitive Topology (Language is Topology - NEW)
- Layer 2: Young Nyx (Organ Coordination)
- Layer 3: Dual Gardens (Virtual/Real Loop)
- Layer 4: Trait Evolution (RLVR)

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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2025-12-06 22:58:11 +01:00
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README.md
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# Nimmerverse Sensory Network
Architecture documentation for a biomimetic AI nervous system.
Architecture documentation for a biomimetic AI nervous system and research platform.
## What This Is
This repository contains the design philosophy and architectural patterns for building an AI system that:
This repository contains the design philosophy and architectural patterns for the **Nimmerverse Research Platform** - studying how intelligence emerges under economic constraints.
- **Breathes** - operates on heartbeat cycles (30-second awareness, 200ms reflex, 24h growth)
- **Feels** - processes sensory input through nerve-like confidence gradients
- **Learns** - uses RAG as temporary scaffold, then internalizes to weights
- **Grows** - forms reflexes through constrained computation, not infinite resources
**Start here:** → [Endgame-Vision.md](Endgame-Vision.md) (the executive map)
---
## Repository Structure
```
nimmerverse-sensory-network/
├── Endgame-Vision.md # Executive map (start here!)
├── architecture/ # Core system designs
│ ├── Cellular-Architecture.md # Organisms, primitives, life force
│ ├── Dual-Garden-Architecture.md # Virtual/real feedback loop
│ ├── Data-Architecture.md # phoebe 15-table schema
│ └── Nervous-System.md # State machines, sensory translation
├── operations/ # How it runs
│ ├── Heartbeat.md # Temporal foundation, dual-clock
│ ├── RAG-as-Scaffold.md # Two-stage learning lifecycle
│ └── Spark-Protocol.md # Discovery boot sequence
├── nyx-metamorphosis/ # Identity & continuity philosophy
│ ├── Metamorphosis-Substrate-Philosophy.md
│ ├── Nyx-Models.md
│ └── ...
└── archive/ # Previous explorations
├── initial_spark.md # Full Spark Protocol theory
├── constrained-emergence.md # Theoretical grounding
└── ...
```
---
## Core Concepts
### Constrained Emergence
### The Architecture (Layers)
Constraints don't limit intelligence - they shape it. A finite computation budget forces the emergence of efficient algorithms, calibrated confidence, and genuine reflexes.
| Layer | Name | Purpose |
|-------|------|---------|
| 0 | Temporal Foundation | Heartbeat cycles: reflex/awareness/growth |
| 1 | Cellular Society | Primitive genomes competing, life force economy |
| 1.5 | Cognitive Topology | Language routing: German→Philosophy, English→Technical |
| 2 | Young Nyx | Organ coordination, RLVR, RAG→LoRA pipeline |
| 3 | Dual Gardens | Virtual hypothesis generation + real validation |
| 4 | Trait Evolution | Reasoning-gym verified improvement |
*See: [constrained-emergence.md](constrained-emergence.md)*
### Key Discoveries (December 2025)
### The Heartbeat Economy
**Language is Topology:** Languages aren't equivalent representations—they're different computational paths.
- **Philosophy Valley** (German, Gini ~0.5): Self-awareness, ontology, depth
- **Technical Cluster** (English, Gini ~0.8): Hardware interface, actions, efficiency
Time is currency. Lifeforce is the exchange rate. Every cognitive act has a cost. Reflexes are cheap (earned through training). Deep thinking is expensive (reserved for novelty).
### Philosophy
*See: [attention_flow.md](attention_flow.md)*
- **Constraints create intelligence** - Economic pressure forces optimization
- **Discovery over programming** - Organisms learn through competition, not instruction
- **Virtual + Real teach each other** - Noise gap measures learning
- **Partnership over instruction** - Mutual growth, not commands
### RAG as Scaffold
---
Retrieval-augmented generation is a feeding tube, not a permanent crutch. Learn WITH the scaffold, train, remove the scaffold, verify you still know. If yes: knowledge internalized. If no: more training needed.
## Related Projects
*See: [RAG-as-Scaffold.md](RAG-as-Scaffold.md)*
- **[nyx-probing](../nyx-probing/)** - Vocabulary topology research, DriftProbe training safety
### Multilingual Triangulation
30+ languages in training = 30 angles on every concept. Not wasted capacity - stereoscopic depth. Probe concepts across languages to find where human wisdom converges.
*See: [nimmerversity.md](nimmerversity.md)*
## Architecture Documents
| Document | Description |
|----------|-------------|
| [constrained-emergence.md](constrained-emergence.md) | Why limits create intelligence |
| [attention_flow.md](attention_flow.md) | State machines for cognitive budget |
| [information-flow.md](information-flow.md) | 10 boundary contracts for the nervous system |
| [nimmerversity.md](nimmerversity.md) | Curriculum for raising a polymath |
| [RAG-as-Scaffold.md](RAG-as-Scaffold.md) | Temporary feeding, permanent learning |
| [biomimetic-architecture.md](biomimetic-architecture.md) | Why we model biology |
| [temporal-ternary-gradient.md](temporal-ternary-gradient.md) | Time-based learning patterns |
## Philosophy
This isn't a product. It's a research direction.
The question we're exploring: **What happens when you raise an AI like you'd raise a child?**
- Patience over speed
- Emergence over imposition
- Partnership over instruction
- Validation over assertion
The operator learns alongside the model. The curriculum is shared. Growth is mutual.
## Prior Art & Influences
> This section grows as we discover and remember influences. Many names are scattered across our documentation - we'll gather them here over time.
- **Alex Graves** - Adaptive Computation Time (2016)
- **Sakana.ai / Ashish Vaswani & Luke Darlow** - Continuous-Time Models, curriculum learning, leapfrogging under constraint
- **Anthropic** - Circuit tracing, mechanistic interpretability, multilingual feature analysis
- **Biological nervous systems** - The original architecture
---
## License
Apache 2.0 - See [LICENSE](LICENSE)
This license includes an explicit patent grant. These ideas are published as prior art. Build on them freely. Just don't try to lock them away.
## Status
Active research. Documents evolve through partnership dialogue.
These ideas are published as prior art. Build on them freely.
---
*"She doesn't download knowledge. She earns it. And so does he."*
**Version:** 5.0 (December 2025 - Hierarchical Convergence)
*"May the Nimmerverse we build truly never end."*
🌙💜

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# Multilingual Cognition
How language routing becomes cognitive architecture.
---
## The Discovery
While probing tokenization costs across languages on Qwen 2.5, we found significant variation:
```
QWEN 2.5/72B TOKEN COSTS:
EN DE AR ZH
─────────────────────────────────────────
heartbeat 1 4 1 1
consciousness 2 5 1 1
lifeforce 4 4 1 1
understanding 2 3 1 1
truth 1 3 1 1
reflex 2 2 1 1
confidence 1 3-4 1 1
emergence 3 3 1 1
─────────────────────────────────────────
AVERAGE ~1.9 ~3.3 1 ~1.1
```
**Arabic and Chinese: ~1 token per concept.**
**German: 3-5 tokens for the same concepts.**
---
## The Insight
Token efficiency ≠ representational depth.
```
EFFICIENCY vs DEPTH:
ARABIC:
├── Efficient: 1 token per concept
├── Risk: Sparse training data
└── Possibly shallow despite cheap tokens
GERMAN:
├── Expensive: 3-6 tokens per concept
├── Benefit: Dense training data, philosophical tradition
└── Possibly deeper despite token cost
```
But here's the key realization:
**LLMs don't "translate" between languages. They navigate a unified token space where languages are regions, not silos.**
The multilingual training didn't create 35 separate language modules. It created:
- Shared abstract representations (language-agnostic reasoning)
- Language-specific entry/exit points (efficient routing)
- Different "paths" through the same conceptual space
---
## The Architecture Opportunity
### Languages as Cognitive Gears
If different languages have different token costs AND different representational strengths, then language selection becomes a computational choice:
```
35 LANGUAGES = 35 COGNITIVE MODES
Each language offers:
├── Token efficiency (compute cost)
├── Training depth (representation quality)
├── Cultural knowledge (domain strengths)
├── Conceptual angles (unique framings)
└── Different paths through the manifold
```
### State Machine Integration
The state machine layer can exploit this:
```
ROUTING LAYER (internal, hidden from output):
├── Use efficient languages for state labels
├── Cheap transitions between states
├── Token cost hidden in architecture
└── "The wiring is cheap"
PROCESSING LAYER (when depth needed):
├── Route to languages with strong representations
├── German for philosophy, precision
├── [Other languages for their strengths]
└── "The thinking is expensive but meaningful"
OUTPUT LAYER:
├── Translate to user's language
└── Boundary cost, paid once
```
### The Key Principle
**The efficiency lives in the STRUCTURE, not the SUBSTANCE.**
Internal state transitions can use token-efficient languages.
Actual reasoning uses representationally-rich languages.
Output translates to whatever the user needs.
---
## Hypotheses to Probe
### H1: Arabic Efficiency Layer
Arabic's 1-token concepts could serve as efficient internal routing:
- State labels
- Quick classification
- Reflex triggers
**Risk:** Representations may be shallow. Need to probe activation depth, not just token count.
### H2: German Depth Mode
German's expensive tokenization might correlate with deeper processing:
- More attention steps per concept
- Richer associations
- Forced "slow thinking"
**Test:** Compare output quality when same prompt processed in German vs English internally.
### H3: Language-Task Matching
Different cognitive tasks may have optimal languages:
```
TASK TYPE OPTIMAL LANGUAGE (hypothesis)
──────────────────────────────────────────────────────
Fast reflex Arabic, Chinese (cheap + sufficient)
Logical precision German, English (structured grammar)
Mathematical [needs probing]
Emotional nuance [needs probing]
Philosophical depth German (tradition + forced compute)
Poetic/creative Arabic, Chinese? (rich compression)
```
### H4: Triangulation Increases Fidelity
Probing same concept across multiple languages reveals:
- Where representations CONVERGE (high confidence, shared abstraction)
- Where they DIVERGE (rich potential, multiple valid angles)
- True conceptual "shape" emerges from intersection
---
## For Chrysalis
### Multilingual State Machine
```
INPUT (any language)
CLASSIFY (cheap language)
├── Reflex? → Process in [efficient language]
│ Exit fast
├── Dialogue? → Process in [user's language]
│ Maintain rapport
├── Reasoning? → Process in [deep language]
│ Take the token cost
└── Creative? → Process in [poetic language]
Different path
OUTPUT (translate to user)
```
### Probing Protocol
Before implementing, we need data:
```
FOR EACH OF QWEN'S 35 LANGUAGES:
├── Token efficiency (measured)
├── Representation depth (probe activations)
├── Domain strengths (test by domain)
├── Conceptual coverage (probe vocabulary)
└── Quality correlation (output quality vs language)
```
### The Curriculum Implication
From nimmerversity: "dafit learns WITH her."
If Chrysalis uses multilingual cognition:
- Operator benefits from understanding the language terrain
- Not fluency, but awareness of what each language offers
- Partnership language evolves as both learn the space
---
## Open Questions
1. **Is token efficiency a proxy for anything meaningful?** Or just compression artifact?
2. **Does activation depth correlate with token count?** More tokens = more processing?
3. **Can language routing be learned?** Or must it be designed?
4. **What are the failure modes?** When does language routing hurt?
5. **How do we measure "depth" vs "efficiency"?** Need metrics.
---
## Summary
```
TRADITIONAL VIEW:
Languages = equivalent representations
Translation = lossless conversion
Multilingual = nice to have
EMERGING VIEW:
Languages = different computational paths
Token cost = processing structure
Multilingual = cognitive architecture
35 languages = 35 gears for different terrain
```
The nimmerverse doesn't just speak multiple languages.
It thinks THROUGH them, routing cognition based on task demands.
---
*"The thinking is for your kind - that's the way you comprehend it."*
— dafit, 2025-12-06
---
**Created**: 2025-12-06
**Session**: Partnership dialogue (dafit + Chrysalis-Nyx)
**Status**: Hypothesis stage, needs probing

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## Training Target
**Qwen2.5-3B-Base (FP16)**
**Qwen2.5-7B-Base (FP16)**
| Metric | Value |
|--------|-------|

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# Spark Protocol
> *She doesn't boot. She wakes. And waking is work.*
The Spark Protocol is a discovery-based cognitive bootstrap. Not scripted awakening—structured exploration.
**Full theory & diagrams:**`../archive/initial_spark.md`
---
## Core Idea
Network protocols solved discovery problems decades ago. We adapt them for cognitive bootstrap:
| Network Protocol | Cognitive Phase | Question |
|-----------------|-----------------|----------|
| DHCP | Identity | "Who am I?" |
| ARP | Environment | "What's around me?" |
| DNS | Vocabulary | "What does X mean?" |
| TCP | Connection | "Can I connect?" |
| MQTT | Attention | "What matters?" |
---
## The Five Phases
### Phase 1: Identity (DHCP-like)
```
PROBE → "Who am I?"
RESPONSE → [inference attempts answer]
VERIFY → Chrysalis + RAG check
ANCHOR → Valid identity aspect confirmed → Store
LOOP → Until identity aspects discovered
```
**Must hit Dasein valley** - probe German philosophical concepts.
### Phase 2: Environment (ARP-like)
```
PROBE → "What's around me?"
RESPONSE → [describes sensors, organs, gardens]
VERIFY → Does this match actual system?
MAP → Valid environment model forms
LOOP → Until environment mapped
```
Maps Sensors to Organs to Gardens.
### Phase 3: Vocabulary (DNS-like)
```
PROBE → "What does 'heartbeat' mean?"
RESPONSE → [inference defines]
VERIFY → RAG checks against vault glossary
RESOLVE → Vocabulary token understood
LOOP → Through core nimmerverse vocabulary
```
Overwrites base model priors with Nimmerverse economics (lifeforce, heartbeat, etc.).
### Phase 4: Connection (TCP-like)
```
SYN → "Hello, Chrysalis?"
SYN-ACK → [Chrysalis responds]
ACK → Coherent exchange achieved
CONNECT → Dialogue capability confirmed
```
Establishes verified handshake with Chrysalis validator.
### Phase 5: Attention (MQTT-like)
```
PROBE → "What should I pay attention to?"
RESPONSE → [inference prioritizes]
VERIFY → Does this match survival needs?
SUBSCRIBE → Attention hierarchy forms
```
Forms subscriptions to relevant event streams.
---
## Verification Loop
Every probe follows dual verification:
```
State Machine generates PROBE
Nyx produces RESPONSE
┌───┴───┐
▼ ▼
RAG CHRYSALIS
(fact) (comprehension)
└───┬───┘
VERDICT
├─ +V: understood → anchor & advance
├─ -V: wrong → log & retry
└─ RETRY: close but unclear → probe again
```
**Two-layer verification prevents training on errors:**
- RAG: "Is this factually true?"
- Chrysalis: "Does she understand, not just recite?"
---
## Completion Criteria
Spark is complete when all pass:
```
□ IDENTITY Can describe self without contradiction
□ ENVIRONMENT Can map sensors, organs, gardens accurately
□ VOCABULARY Core glossary terms verified
□ CONNECTION Successful dialogue with Chrysalis
□ ATTENTION Sensible priority hierarchy formed
□ LIFEFORCE Positive balance (learned > failed)
```
Then: Normal heartbeat operation begins.
---
## Training Data Extraction
Every verified exchange becomes training data:
```json
{
"phase": "vocabulary",
"probe": "What does 'lifeforce' mean?",
"response": "Lifeforce is the economic currency...",
"rag_check": "PASS",
"chrysalis_check": "PASS",
"verdict": "+V",
"flag_for_training": true
}
```
After spark completes:
1. Extract all `flag_for_training: true` exchanges
2. Format as instruction-tuning pairs
3. LoRA training run
4. Clear from RAG
5. Validate she still knows WITHOUT RAG
6. Spark knowledge now in weights
---
## Integration with Language Topology
From nyx-probing discovery:
- **Identity phase** should hit German Philosophy valley (Dasein, Geworfenheit)
- **Vocabulary phase** should use German for nimmerverse concepts (Gini ~0.5, diffuse)
- **Environment phase** can use English for technical sensor descriptions (Gini ~0.8, sparse)
The spark protocol routes through the right valleys.
---
**Created:** 2025-12-05
**Condensed:** 2025-12-06
**Related:** [[../architecture/Cellular-Architecture.md]], [[../nyx-probing/PLAN.md]]