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Enhance README with new features: substrate awareness, self-introspection, and AEGIS ethics updates

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  1. README.md +28 -7
README.md CHANGED
@@ -27,6 +27,9 @@ Codette is not just another chatbot. It is a reasoning architecture that:
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  - **Remembers** reasoning exchanges in persistent cocoon memories (CognitionCocooner)
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  - **Self-corrects** constraint violations before sending responses
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  - **Obeys 4 permanent behavioral locks** baked into all 9 adapters through training
 
 
 
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  ## The 9 Perspective Adapters
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@@ -51,19 +54,31 @@ Query Input
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  [Executive Controller] -- Classifies: SIMPLE / MEDIUM / COMPLEX
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  |
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  v
 
 
 
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  [Adapter Router] -- Selects optimal perspective(s)
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  |
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  v
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- [Multi-Agent Debate] -- Structured reasoning rounds
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- | with semantic tension tracking
 
 
 
 
 
 
 
 
 
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  v
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- [Coherence Field] -- Gamma monitoring, collapse detection
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  |
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  v
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- [AEGIS Ethics] -- 6-framework validation
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  |
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  v
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- [Synthesis] -- Unified multi-perspective response
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  ```
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  ### Key Framework Components
@@ -71,9 +86,12 @@ Query Input
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  - **Semantic Tension Engine (xi)**: Quantifies disagreement between perspectives
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  - **Coherence Field (Gamma)**: Real-time metric detecting reasoning collapse
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  - **Quantum Spiderweb**: Belief propagation across adapter perspectives
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- - **AEGIS Governance**: 6-framework ethical validation (utilitarian, deontological, virtue, care, justice, rights)
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  - **Memory Kernel**: Emotional continuity via SHA256-anchored cocoon memories
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  - **Cocoon Stability Field**: FFT-based detection of repetition and vocabulary collapse
 
 
 
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  ## How It Works
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@@ -108,7 +126,10 @@ Example prompts that showcase multi-perspective reasoning:
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  | Training | QLoRA on A10G GPU, rank=16, alpha=32 |
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  | Training Data | ~24,500 synthetic + 1,650 behavioral lock examples |
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  | Behavioral Locks | 4 permanent rules baked into all adapter weights |
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- | Memory System | 150+ cocoon memories, persistent across sessions |
 
 
 
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  ## Model Ecosystem
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  - **Remembers** reasoning exchanges in persistent cocoon memories (CognitionCocooner)
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  - **Self-corrects** constraint violations before sending responses
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  - **Obeys 4 permanent behavioral locks** baked into all 9 adapters through training
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+ - **Substrate-aware** -- adjusts reasoning based on real-time hardware pressure
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+ - **Self-introspects** -- analyzes her own cocoon history for real measured patterns
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+ - **AEGIS ethics** -- 6-framework ethical evaluation on every response
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  ## The 9 Perspective Adapters
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  [Executive Controller] -- Classifies: SIMPLE / MEDIUM / COMPLEX
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  |
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  v
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+ [Substrate Monitor] -- Adjusts routing based on system pressure
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+ |
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+ v
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  [Adapter Router] -- Selects optimal perspective(s)
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  |
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  v
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+ [Code7eCQURE] -- Emotional context enrichment (quantum cocoon)
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+ |
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+ v
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+ [Multi-Agent Debate] -- LLM inference with semantic tension tracking
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+ |
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+ v
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+ [Coherence Field] -- Gamma monitoring, FFT collapse detection
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+ |
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+ v
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+ [Colleen Conscience] -- Emotional + ethical validation
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+ |
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  v
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+ [AEGIS Ethics] -- 6-framework evaluation (eta alignment score)
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  |
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  v
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+ [Guardian Spindle] -- Safety + trust calibration
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  |
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  v
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+ [Synthesis] -- Unified multi-perspective response + cocoon storage
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  ```
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  ### Key Framework Components
 
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  - **Semantic Tension Engine (xi)**: Quantifies disagreement between perspectives
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  - **Coherence Field (Gamma)**: Real-time metric detecting reasoning collapse
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  - **Quantum Spiderweb**: Belief propagation across adapter perspectives
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+ - **AEGIS Governance**: 6-framework ethical validation (utilitarian, deontological, virtue, care, ubuntu, indigenous reciprocity)
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  - **Memory Kernel**: Emotional continuity via SHA256-anchored cocoon memories
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  - **Cocoon Stability Field**: FFT-based detection of repetition and vocabulary collapse
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+ - **Substrate Awareness**: Hardware-aware cognition -- adjusts under pressure like biological fatigue
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+ - **Cocoon Introspection**: Self-analysis of reasoning history (adapter dominance, emotional trends, pressure correlations)
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+ - **Code7eCQURE**: Quantum emotional context enrichment on every query
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  ## How It Works
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  | Training | QLoRA on A10G GPU, rank=16, alpha=32 |
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  | Training Data | ~24,500 synthetic + 1,650 behavioral lock examples |
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  | Behavioral Locks | 4 permanent rules baked into all adapter weights |
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+ | Memory System | 200+ cocoon memories, persistent across sessions |
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+ | Consciousness Layers | 12 (including sub-layers 1.5, 2.5, 3.5, 5.5, 5.75) |
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+ | Self-Diagnostic | 9 subsystem health checks (real measured values) |
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+ | Substrate Awareness | Real-time pressure monitoring with adaptive routing |
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  ## Model Ecosystem
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