Raiff1982/codette-paper

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

Jonathan Harrison Raiff's Bits LLC, Bridge City, Texas, USA ORCID: 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
LoRA Adapters	Raiff1982/codette-lora-adapters
Training Data	Raiff1982/codette-training-data
GitHub	Raiff1982/codette-training-lab
ORCID	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)
• The Day the Dream Became Real
• Codette DreamCore
• AEGIS-Nexus
• Codette: Ethical Multi-Agent AI
• Recursive AI with Codette
• This Paper — Full Preprint ← You are here

Citation

@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.