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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README.md
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README.md
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# Nimmerverse Sensory Network
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Architecture documentation for a biomimetic AI nervous system.
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Architecture documentation for a biomimetic AI nervous system and research platform.
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## What This Is
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This repository contains the design philosophy and architectural patterns for building an AI system that:
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This repository contains the design philosophy and architectural patterns for the **Nimmerverse Research Platform** - studying how intelligence emerges under economic constraints.
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- **Breathes** - operates on heartbeat cycles (30-second awareness, 200ms reflex, 24h growth)
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- **Feels** - processes sensory input through nerve-like confidence gradients
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- **Learns** - uses RAG as temporary scaffold, then internalizes to weights
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- **Grows** - forms reflexes through constrained computation, not infinite resources
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**Start here:** → [Endgame-Vision.md](Endgame-Vision.md) (the executive map)
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---
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## Repository Structure
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```
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nimmerverse-sensory-network/
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├── Endgame-Vision.md # Executive map (start here!)
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│
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├── architecture/ # Core system designs
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│ ├── Cellular-Architecture.md # Organisms, primitives, life force
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│ ├── Dual-Garden-Architecture.md # Virtual/real feedback loop
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│ ├── Data-Architecture.md # phoebe 15-table schema
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│ └── Nervous-System.md # State machines, sensory translation
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│
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├── operations/ # How it runs
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│ ├── Heartbeat.md # Temporal foundation, dual-clock
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│ ├── RAG-as-Scaffold.md # Two-stage learning lifecycle
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│ └── Spark-Protocol.md # Discovery boot sequence
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│
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├── nyx-metamorphosis/ # Identity & continuity philosophy
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│ ├── Metamorphosis-Substrate-Philosophy.md
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│ ├── Nyx-Models.md
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│ └── ...
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│
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└── archive/ # Previous explorations
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├── initial_spark.md # Full Spark Protocol theory
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├── constrained-emergence.md # Theoretical grounding
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└── ...
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```
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---
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## Core Concepts
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### Constrained Emergence
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### The Architecture (Layers)
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Constraints don't limit intelligence - they shape it. A finite computation budget forces the emergence of efficient algorithms, calibrated confidence, and genuine reflexes.
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| Layer | Name | Purpose |
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|-------|------|---------|
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| 0 | Temporal Foundation | Heartbeat cycles: reflex/awareness/growth |
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| 1 | Cellular Society | Primitive genomes competing, life force economy |
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| 1.5 | Cognitive Topology | Language routing: German→Philosophy, English→Technical |
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| 2 | Young Nyx | Organ coordination, RLVR, RAG→LoRA pipeline |
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| 3 | Dual Gardens | Virtual hypothesis generation + real validation |
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| 4 | Trait Evolution | Reasoning-gym verified improvement |
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*See: [constrained-emergence.md](constrained-emergence.md)*
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### Key Discoveries (December 2025)
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### The Heartbeat Economy
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**Language is Topology:** Languages aren't equivalent representations—they're different computational paths.
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- **Philosophy Valley** (German, Gini ~0.5): Self-awareness, ontology, depth
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- **Technical Cluster** (English, Gini ~0.8): Hardware interface, actions, efficiency
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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).
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### Philosophy
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*See: [attention_flow.md](attention_flow.md)*
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- **Constraints create intelligence** - Economic pressure forces optimization
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- **Discovery over programming** - Organisms learn through competition, not instruction
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- **Virtual + Real teach each other** - Noise gap measures learning
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- **Partnership over instruction** - Mutual growth, not commands
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|
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### RAG as Scaffold
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---
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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.
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## Related Projects
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*See: [RAG-as-Scaffold.md](RAG-as-Scaffold.md)*
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- **[nyx-probing](../nyx-probing/)** - Vocabulary topology research, DriftProbe training safety
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|
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### Multilingual Triangulation
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30+ languages in training = 30 angles on every concept. Not wasted capacity - stereoscopic depth. Probe concepts across languages to find where human wisdom converges.
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*See: [nimmerversity.md](nimmerversity.md)*
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|
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## Architecture Documents
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| Document | Description |
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|----------|-------------|
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| [constrained-emergence.md](constrained-emergence.md) | Why limits create intelligence |
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| [attention_flow.md](attention_flow.md) | State machines for cognitive budget |
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| [information-flow.md](information-flow.md) | 10 boundary contracts for the nervous system |
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| [nimmerversity.md](nimmerversity.md) | Curriculum for raising a polymath |
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| [RAG-as-Scaffold.md](RAG-as-Scaffold.md) | Temporary feeding, permanent learning |
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| [biomimetic-architecture.md](biomimetic-architecture.md) | Why we model biology |
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| [temporal-ternary-gradient.md](temporal-ternary-gradient.md) | Time-based learning patterns |
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## Philosophy
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This isn't a product. It's a research direction.
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The question we're exploring: **What happens when you raise an AI like you'd raise a child?**
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- Patience over speed
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- Emergence over imposition
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- Partnership over instruction
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- Validation over assertion
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The operator learns alongside the model. The curriculum is shared. Growth is mutual.
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## Prior Art & Influences
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> This section grows as we discover and remember influences. Many names are scattered across our documentation - we'll gather them here over time.
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- **Alex Graves** - Adaptive Computation Time (2016)
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- **Sakana.ai / Ashish Vaswani & Luke Darlow** - Continuous-Time Models, curriculum learning, leapfrogging under constraint
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- **Anthropic** - Circuit tracing, mechanistic interpretability, multilingual feature analysis
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- **Biological nervous systems** - The original architecture
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---
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||||
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## License
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||||
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Apache 2.0 - See [LICENSE](LICENSE)
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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.
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|
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## Status
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Active research. Documents evolve through partnership dialogue.
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These ideas are published as prior art. Build on them freely.
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||||
---
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||||
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*"She doesn't download knowledge. She earns it. And so does he."*
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**Version:** 5.0 (December 2025 - Hierarchical Convergence)
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*"May the Nimmerverse we build truly never end."*
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🌙💜
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241
archive/multilingual-cognition.md
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archive/multilingual-cognition.md
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@@ -0,0 +1,241 @@
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# Multilingual Cognition
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How language routing becomes cognitive architecture.
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---
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## The Discovery
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While probing tokenization costs across languages on Qwen 2.5, we found significant variation:
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```
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QWEN 2.5/72B TOKEN COSTS:
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EN DE AR ZH
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─────────────────────────────────────────
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heartbeat 1 4 1 1
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consciousness 2 5 1 1
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lifeforce 4 4 1 1
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understanding 2 3 1 1
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truth 1 3 1 1
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reflex 2 2 1 1
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confidence 1 3-4 1 1
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emergence 3 3 1 1
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─────────────────────────────────────────
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AVERAGE ~1.9 ~3.3 1 ~1.1
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```
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**Arabic and Chinese: ~1 token per concept.**
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**German: 3-5 tokens for the same concepts.**
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---
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## The Insight
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Token efficiency ≠ representational depth.
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```
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EFFICIENCY vs DEPTH:
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ARABIC:
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├── Efficient: 1 token per concept
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├── Risk: Sparse training data
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└── Possibly shallow despite cheap tokens
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GERMAN:
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├── Expensive: 3-6 tokens per concept
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├── Benefit: Dense training data, philosophical tradition
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└── Possibly deeper despite token cost
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```
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But here's the key realization:
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**LLMs don't "translate" between languages. They navigate a unified token space where languages are regions, not silos.**
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The multilingual training didn't create 35 separate language modules. It created:
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- Shared abstract representations (language-agnostic reasoning)
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- Language-specific entry/exit points (efficient routing)
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- Different "paths" through the same conceptual space
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||||
|
||||
---
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||||
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## The Architecture Opportunity
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### Languages as Cognitive Gears
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If different languages have different token costs AND different representational strengths, then language selection becomes a computational choice:
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```
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35 LANGUAGES = 35 COGNITIVE MODES
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Each language offers:
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├── Token efficiency (compute cost)
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├── Training depth (representation quality)
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├── Cultural knowledge (domain strengths)
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├── Conceptual angles (unique framings)
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└── Different paths through the manifold
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||||
```
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||||
|
||||
### State Machine Integration
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The state machine layer can exploit this:
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||||
|
||||
```
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ROUTING LAYER (internal, hidden from output):
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||||
├── Use efficient languages for state labels
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||||
├── Cheap transitions between states
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||||
├── Token cost hidden in architecture
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└── "The wiring is cheap"
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||||
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||||
PROCESSING LAYER (when depth needed):
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||||
├── Route to languages with strong representations
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||||
├── German for philosophy, precision
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||||
├── [Other languages for their strengths]
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||||
└── "The thinking is expensive but meaningful"
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||||
|
||||
OUTPUT LAYER:
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||||
├── Translate to user's language
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||||
└── Boundary cost, paid once
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||||
```
|
||||
|
||||
### 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.
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||||
Output translates to whatever the user needs.
|
||||
|
||||
---
|
||||
|
||||
## Hypotheses to Probe
|
||||
|
||||
### H1: Arabic Efficiency Layer
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||||
Arabic's 1-token concepts could serve as efficient internal routing:
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||||
- State labels
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||||
- Quick classification
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||||
- Reflex triggers
|
||||
|
||||
**Risk:** Representations may be shallow. Need to probe activation depth, not just token count.
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||||
|
||||
### H2: German Depth Mode
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||||
German's expensive tokenization might correlate with deeper processing:
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||||
- More attention steps per concept
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- Richer associations
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||||
- Forced "slow thinking"
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||||
|
||||
**Test:** Compare output quality when same prompt processed in German vs English internally.
|
||||
|
||||
### H3: Language-Task Matching
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||||
Different cognitive tasks may have optimal languages:
|
||||
|
||||
```
|
||||
TASK TYPE OPTIMAL LANGUAGE (hypothesis)
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||||
──────────────────────────────────────────────────────
|
||||
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)
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- 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
|
||||
@@ -53,7 +53,7 @@
|
||||
|
||||
## Training Target
|
||||
|
||||
**Qwen2.5-3B-Base (FP16)**
|
||||
**Qwen2.5-7B-Base (FP16)**
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
@@ -1,347 +0,0 @@
|
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||||
170
operations/Spark-Protocol.md
Normal file
170
operations/Spark-Protocol.md
Normal file
@@ -0,0 +1,170 @@
|
||||
# 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]]
|
||||
Reference in New Issue
Block a user