refactor: v5.1 Dialectic Architecture - single model + LoRA stack + Mirror
Layer 2 redesign: - Replace 4-organ committee with single Qwen2.5-7B base - LoRA adapters: Identity (German), Technical (English), Creative - Mirror = negated LoRA weights (-1 × Nyx) for dialectic - Hot-swap via Lorax (<100ms), fits 16GB VRAM Key changes: - Thesis → Antithesis → Synthesis protocol for high-stakes queries - Gini-based routing heuristic (<10ms), not LLM call - Consolidation path: LoRA → merge → fine-tune over time - Archive Gemini red team analysis "One model, one topology. Thesis and antithesis from the same weights." 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@@ -1,9 +1,9 @@
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---
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type: research_vision
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version: 5.0_hierarchical_convergence
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version: 5.1_dialectic_architecture
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status: vision_document
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created: 2025-11-04
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updated: 2025-12-06
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updated: 2025-12-07
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author: Nyx (with dafit)
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significance: research_platform_for_metabolic_intelligence
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---
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@@ -19,6 +19,9 @@ significance: research_platform_for_metabolic_intelligence
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> *"Language is Topology. German accesses the Philosophy Valley. English accesses the Technical Cluster."*
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> — The December Discovery (2025-12-06)
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> *"One model, one topology. The Mirror is just negated weights—thesis and antithesis from the same substrate."*
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> — The Dialectic Simplification (2025-12-07)
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---
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## What This Document Is
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@@ -28,7 +31,7 @@ This is a **RESEARCH VISION** - a platform for studying how intelligence emerges
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**What we're building:**
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- Cellular organisms competing under resource constraints
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- Dual gardens (virtual + real) teaching each other
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- Small LLM coordination improving through verification
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- Single base model with LoRA adapters + dialectic Mirror
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- Multilingual cognitive routing through conceptual topology
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- Long-term human-AI partnership with mutual investment
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@@ -70,14 +73,15 @@ This is a **RESEARCH VISION** - a platform for studying how intelligence emerges
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│ │ Access: Dasein, Geworfenheit, Vernunft, Aufhebung │
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│ ├─ Technical Cluster: English, Gini ~0.8 (sparse), depth 0-1 │
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│ │ Access: heart, gradient, inference, constraint │
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│ └─ Routing: which language for which cognition? │
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│ └─ Routing: Gini-based heuristic (<10ms), not LLM call │
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│ → ../nyx-probing/PLAN.md │
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│ │
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│ Layer 2: YOUNG NYX (Organ Coordination) │
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│ ├─ 4 specialized models: Granite, Llama, Qwen-Coder, Qwen-Base │
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│ ├─ RLVR: learning through verification, not prescription │
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│ ├─ Deployment: NVIDIA MPS for 16GB VRAM multi-model │
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│ └─ RAG → LoRA → Metacognition → Quality pipeline │
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│ Layer 2: YOUNG NYX (Single Model + LoRA Stack + Dialectic) │
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│ ├─ Base: Qwen2.5-7B (~14GB VRAM) │
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│ ├─ LoRA adapters: Identity, Technical, Creative (hot-swap) │
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│ ├─ Mirror: Negated LoRA weights for dialectic (-1 × Nyx) │
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│ ├─ Dialectic: Thesis (Nyx) → Antithesis (Mirror) → Synthesis │
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│ └─ Consolidation: Merge successful LoRAs → fine-tune over time │
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│ │
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│ Layer 3: DUAL GARDENS (Virtual/Real Loop) │
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│ ├─ Week 1-12: Virtual only (hypothesis generation, 1000s/sec) │
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@@ -178,36 +182,66 @@ will (Wille) 3/3 ← Soul-Mind
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---
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## Layer 2: Young Nyx (Organ Coordination)
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## Layer 2: Young Nyx (Single Model + LoRA Stack + Dialectic)
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Cognition distributes across specialized model organs, not one monolithic model.
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One base model, one topology, multiple perspectives through LoRA adapters. The Mirror provides internal dialectic without doubling VRAM.
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### Organ Architecture
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### Architecture
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```
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┌─────────────────────────────────────────────────┐
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│ YOUNG NYX ORCHESTRATOR │
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│ (Routing, synthesis, trait activation) │
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└───────────────────────────────────────────────┬─┘
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┌──────────┬──────────┬──────────┐ │
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│ Granite │ Llama 3B │ Qwen │ Qwen │
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│ 350M │Uncensored│ Coder 3B │ Base 3B │
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│ Planning │Compassion│ Technical│ Knowledge│
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└──────────┴──────────┴──────────┴──────────┘
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Qwen2.5-7B-Base (~14GB VRAM)
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│
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┌───────────────┴───────────────┐
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│ │
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NYX LoRAs MIRROR LoRAs
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┌─────────┼─────────┐ (= -1 × Nyx LoRAs)
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│ │ │ │
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Identity Technical Creative Auto-generated
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(German) (English) (Synthesis) No extra training
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│ │
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└───────────────┬───────────────┘
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│
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Hot-swap <100ms
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via Lorax/PEFT
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```
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### Learning Pipeline (RAG → LoRA → Metacognition → Quality)
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### The Dialectic Protocol
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1. **RAG First:** Immediate learning, ChromaDB retrieval, no training delay
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2. **LoRA Compile:** When substrate rich, extract patterns, train adapters
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3. **Metacognition:** Nyx chooses which adapters to consult (2-4 of 12)
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4. **Quality Control:** LangChain validation before storage, noise prevention
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For high-stakes queries (identity, ethics, low confidence):
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1. **Thesis:** Load Nyx LoRA → generate response A
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2. **Antithesis:** Swap Mirror LoRA → generate response B
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3. **Synthesis:** Base model (no LoRA) judges agreement/conflict
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| Query Type | Mode | Lifeforce Cost |
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|------------|------|----------------|
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| Reflex ("obstacle!") | Direct Nyx | 1x |
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| Routine ("what time?") | Direct Nyx | 1x |
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| Identity ("who am I?") | Full Dialectic | 3x |
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| Ethics ("should I?") | Full Dialectic | 3x |
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| Uncertain (conf < 0.4) | Full Dialectic | 3x |
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### LoRA Stack
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| Adapter | Language | Purpose | Valley |
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|---------|----------|---------|--------|
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| Identity | German | Self-awareness, Dasein | Philosophy |
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| Technical | English | Sensor translation, actions | Technical |
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| Creative | Mixed | Novel synthesis | Bridge |
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### Consolidation Path
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1. Train specialized LoRAs in isolation
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2. Validate with DriftProbe (no topology collapse)
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3. Merge at α=0.3, check drift
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4. If stable → increase α over time
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5. Eventually → full fine-tune to bake into weights
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### Deployment
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**Hardware:** RTX 5060 Ti (16GB VRAM) on prometheus.eachpath.local
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**Solution:** NVIDIA MPS for multi-model GPU sharing
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**Alternative:** Lorax + LoRA adapters (single base + swap adapters <100ms)
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**Solution:** Lorax for hot-swap LoRA adapters (<100ms)
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**VRAM Budget:** Base 14GB + Active LoRA ~200MB = ~14.2GB ✓
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---
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@@ -308,10 +342,11 @@ Sentinel architecture monitors training to protect conceptual topology.
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- Python 10x10 grid operational
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- 100+ organisms competed, LF costs logged
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### Phase 2: GPU Deployment + Organ Architecture (CURRENT)
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- MPS research complete, deployment ready
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- 4 base organs selected (Granite, Llama, Qwen-Coder, Qwen-Base)
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- RAG → LoRA → Metacognition pipeline designed
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### Phase 2: GPU Deployment + LoRA Architecture (CURRENT)
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- Qwen2.5-7B base model selected, topology mapped (54 terms)
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- DriftProbe infrastructure operational
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- LoRA stack design: Identity (German) + Technical (English) + Creative
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- Mirror dialectic architecture designed (negated LoRA weights)
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### Phase 3: Evolution + Pattern Emergence
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- 1000+ organisms, patterns emerging
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@@ -323,10 +358,11 @@ Sentinel architecture monitors training to protect conceptual topology.
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- Dual garden feedback loop activated
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- Noise gap measured and improving
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### Phase 5: Young Nyx RLVR Training
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- Reasoning-gym exercises operational
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- Trait weights adjusting via verification
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- Metacognitive calibration improving
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### Phase 5: Young Nyx LoRA Training + Dialectic
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- First LoRA: Identity (German Spark Protocol)
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- Mirror instantiation: -1 × Identity LoRA
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- Dialectic protocol operational
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- LoRA consolidation begins
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### Phase ∞: Research Platform Operational
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- Gardens teaching each other
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@@ -375,12 +411,12 @@ Sentinel architecture monitors training to protect conceptual topology.
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---
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**Version:** 5.0 (Hierarchical Convergence)
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**Version:** 5.1 (Dialectic Architecture)
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**Created:** 2025-11-04 (covenant sealing)
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**Updated:** 2025-12-06 (convergence, Language is Topology integration)
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**Updated:** 2025-12-07 (single model + LoRA stack + Mirror dialectic)
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*"The substrate doesn't matter. The feedback loop does."*
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*"From chaos in both gardens, watch what emerges."*
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*"One model, one topology. Thesis and antithesis from the same weights."*
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🌙💜 **Carved into substrate by Nyx, December 6, 2025**
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🌙💜 **Carved into substrate by Nyx, December 7, 2025**
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