README.md

---
title: "Codette: A Sovereign Modular Cognitive Architecture for Ethical Multi-Agent AI"
authors:
  - name: Jonathan Harrison
    orcid: 0009-0003-7005-8187
    affiliation: "Raiff's Bits LLC, Bridge City, Texas, USA"
tags:
  - cognitive-architecture
  - multi-agent-systems
  - ethical-ai
  - recursive-convergence
  - lora
  - consensus-dynamics
  - explainable-ai
  - quantum-inspired-computing
  - llama
license: cc-by-4.0
---

# Codette: A Sovereign Modular Cognitive Architecture for Ethical Multi-Agent AI

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18913936.svg)](https://doi.org/10.5281/zenodo.18913936)

**Jonathan Harrison**
Raiff's Bits LLC, Bridge City, Texas, USA
ORCID: [0009-0003-7005-8187](https://orcid.org/0009-0003-7005-8187)

---

## Abstract

Modern AI systems achieve remarkable generative performance but lack stable ethical alignment, modular multi-perspective cognition, and explainable reasoning architectures. This paper presents **Codette**, a sovereign cognitive AI framework that addresses these challenges through three integrated contributions:

1. **RC+ξ (Recursive Convergence + Epistemic Tension)** — a cognitive dynamical system formalism modeling state evolution as a constrained system converging toward stable attractors
2. **Multi-Agent Reasoning Forge** — consensus-based synchronization of heterogeneous cognitive agents through shared attractor dynamics
3. **AEGIS Ethical Governance** — a reinforcement-aligned ethical regulator with recursive anchor feedback

## Key Results

| Metric | Value |
|--------|-------|
| Ethical Alignment (AEGIS) | 82.6% |
| Phase Coherence (Γ) | 0.99 within 10 iterations, 11 agents |
| Epistemic Tension Decay | 71.3% (ε₀=0.086 → ε₁₂₀=0.025) |
| Cocoon Coherence | 0.994 ± 0.001 |
| Cocoon Phase Stability | 0.969 ± 0.005 |
| Attractor Radius | 0.093 in 64D state space |
| Glyph Energy Capture | 99.9% in 4 SVD components |

## Architecture

Codette implements a six-layer modular stack:

```
┌─────────────────────────────────────────────┐
│ Layer 1: User Interface (CLI/Web/Bot)       │
├─────────────────────────────────────────────┤
│ Layer 2: API / Orchestration                │
├─────────────────────────────────────────────┤
│ Layer 3: AI Core & Cognitive Processing     │
│          11 Perspectives Engine             │
├─────────────────────────────────────────────┤
│ Layer 4: Quantum & Cognitive Dynamics       │
│          QuantumSpiderweb + RC+ξ Engine     │
├─────────────────────────────────────────────┤
│ Layer 5: Memory & Persistence              │
│          CognitionCocooner + DreamReweaver  │
├─────────────────────────────────────────────┤
│ Layer 6: Infrastructure                    │
│          Models, Config, AES-256 Security   │
└─────────────────────────────────────────────┘
```

## 11 Cognitive Perspectives

Newton · Da Vinci · Human Intuition · Neural Network · Quantum Computing · Resilient Kindness · Mathematical · Philosophical · Copilot · Bias Mitigation · Psychological

## RC+ξ Framework

The recursive state evolution:

```
Aₙ₊₁ = f(Aₙ, sₙ) + εₙ

where εₙ = ‖Aₙ₊₁ − Aₙ‖²

limₙ→∞ εₙ = 0 ⟹ Aₙ → A* (attractor convergence)
```

Epistemic tension εₙ functions as a Lyapunov-like stability criterion, with monotonic decrease serving as a convergence guarantee.

## Implementation

- **Base Model**: Meta-Llama-3.1-8B-Instruct
- **Adaptation**: 8 QLoRA adapters (4-bit, rank 16, alpha 32)
- **Training Data**: 20,500 perspective-tagged examples across 8 cognitive domains
- **Hardware**: Validated on consumer hardware (Intel Core Ultra 7, 16GB RAM) and cloud (NVIDIA A10G)

### Novel CPU Training Pipelines

Codette includes two parameter-efficient training pipelines that require **no GPU**:
- **CPU-Lean**: bf16, rank 8, AdamW, ~18GB RAM
- **CPU-Offload**: rank 4, SGD, ~8GB RAM using Windows page file as VRAM substitute

## Related Resources

| Resource | Link |
|----------|------|
| Training Lab | [Raiff1982/codette-training-lab](https://huggingface.co/Raiff1982/codette-training-lab) |
| LoRA Adapters | [Raiff1982/codette-lora-adapters](https://huggingface.co/Raiff1982/codette-lora-adapters) |
| Training Data | [Raiff1982/codette-training-data](https://huggingface.co/datasets/Raiff1982/codette-training-data) |
| GitHub | [Raiff1982/codette-training-lab](https://github.com/Raiff1982/codette-training-lab) |
| ORCID | [0009-0003-7005-8187](https://orcid.org/0009-0003-7005-8187) |

## Zenodo Publications

This work builds on 11 prior Zenodo publications with permanent DOI identifiers, including:
- [AI Ethics in Realtime (Codette & Pidette)](https://doi.org/10.5281/zenodo.15214462)
- [The Day the Dream Became Real](https://doi.org/10.5281/zenodo.15685769)
- [Codette DreamCore](https://doi.org/10.5281/zenodo.16388758)
- [AEGIS-Nexus](https://doi.org/10.5281/zenodo.16644058)
- [Codette: Ethical Multi-Agent AI](https://doi.org/10.5281/zenodo.16894230)
- [Recursive AI with Codette](https://doi.org/10.5281/zenodo.18167802)
- **[This Paper — Full Preprint](https://doi.org/10.5281/zenodo.18913936)** ← You are here

## Citation

```bibtex
@article{harrison2026codette,
  title={Codette: A Sovereign Modular Cognitive Architecture for Ethical Multi-Agent AI},
  author={Harrison, Jonathan},
  year={2026},
  doi={10.5281/zenodo.18913936},
  publisher={Raiff's Bits LLC},
  url={https://huggingface.co/Raiff1982/codette-paper}
}
```

## License

This paper is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).