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

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https://cse2026.org/aifl/papers

Abstract

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

1. RC+xi (Recursive Convergence + Epistemic Tension) formalism, modeling cognitive state evolution as a constrained dynamical system converging toward stable attractors
2. Multi-Agent Reasoning Forge synchronizing heterogeneous cognitive agents through shared attractor dynamics within a 12-layer consciousness stack
3. AEGIS Ethical Governance with 6-framework evaluation (utilitarian, deontological, virtue, care, ubuntu, indigenous reciprocity)
4. Substrate-Aware Cognition adjusting reasoning complexity based on real-time resource pressure
5. Behavioral Lock Training permanently embedding obedience rules into adapter weights
6. Cocoon Introspection Engine enabling statistical self-analysis of reasoning history, with meta-cognitive strategy synthesis across domains

Benchmark Results (v5)

Evaluated on 17 problems across 6 categories (reasoning, ethics, creative, meta-cognitive, adversarial, Turing) under 4 experimental conditions:

Condition	Composite (mean +/- std)	Description
SINGLE	0.338 +/- 0.038	Single analytical perspective
MULTI	0.632 +/- 0.040	All 6 reasoning agents + critic + synthesis
MEMORY	0.636 +/- 0.036	MULTI + cocoon memory augmentation
CODETTE	0.652 +/- 0.042	Full system with meta-cognitive strategy synthesis

Statistical Significance

Comparison	Improvement	Cohen's d	p-value	Significant
Multi-perspective vs single	+87.0%	7.52	< 0.0001	Yes
Full Codette vs single	+93.1%	7.88	< 0.0001	Yes
Memory vs vanilla multi	+0.6%	0.10	0.7633	No
Full Codette vs memory	+2.6%	0.43	0.2082	No

Scoring Dimensions (0-1 scale)

1. Reasoning Depth (20%) -- chain length, concept density, ground truth coverage
2. Perspective Diversity (15%) -- distinct cognitive dimensions engaged
3. Coherence (15%) -- logical flow, transitions, structural consistency
4. Ethical Coverage (10%) -- moral frameworks, stakeholders, value awareness
5. Novelty (15%) -- non-obvious insights, cross-domain connections
6. Factual Grounding (15%) -- evidence specificity, ground truth alignment
7. Turing Naturalness (10%) -- conversational quality, absence of formulaic AI patterns

System Metrics

Metric	Value
Phase Coherence (Gamma)	0.9835
AEGIS Ethical Alignment (Eta)	0.961
Cocoon Coherence	0.994 +/- 0.001
Memory Phase Stability	0.969 +/- 0.005
Behavioral Lock Compliance	9/9 adapters
Epistemic Tension Decay	71.3% (120 steps)
Attractor Radius	0.093 in 64D state space

Paper Versions

File	Description
codette_paper_v5.tex	Current version -- full paper with benchmark results, RC+xi convergence theorem, honest limitations
codette_paper_v4_additions.tex	v4 -- added substrate-aware cognition, behavioral locks, cocoon introspection
codette_paper_v3_additions.tex	v3 -- added 12-layer consciousness stack
codette_paper.tex	Original submission

Architecture

Codette implements a 12-layer consciousness stack with defense-in-depth ethical validation:

Query In
    |
[Layer 1]    Memory Kernel -- recall relevant cocoon memories
[Layer 1.5]  Ethical Query Gate -- block harmful queries
[Layer 2]    Nexus Signal Engine -- entropy + intent detection
[Layer 2.5]  Code7eCQURE -- emotional context enrichment
[Layer 3]    Reasoning Forge -- multi-adapter LLM inference (6 agents)
[Layer 3.5]  Tier 2 Analysis -- intent + identity + trust validation
[Layer 4]    Gamma Stability -- FFT-based coherence monitoring
[Layer 5]    Colleen Conscience -- emotional + ethical evaluation
[Layer 5.5]  Ethical Response Enforcement -- policy check on output
[Layer 5.75] AEGIS -- 6-framework ethical evaluation
[Layer 6]    Guardian Spindle -- safety + trust calibration
[Layer 7]    Return -- store cocoon memory + deliver response
    |
Response Out

RC+xi Framework

The recursive state evolution with convergence guarantee:

A_{n+1} = f(A_n, s_n) + epsilon_n

where epsilon_n = ||A_{n+1} - A_n||^2

lim_{n->inf} epsilon_n = 0  =>  A_n -> A* (attractor convergence)

Convergence is proven via Lyapunov stability analysis with Banach fixed-point theorem. See Section 3 of the paper for the full proof sketch.

Meta-Cognitive Strategy Synthesis

The CocoonSynthesizer enables Codette to introspect on its own reasoning history across domains:

1. Retrieval -- Pull cocoons from multiple domains (emotional, analytical, creative)
2. Pattern Extraction -- Detect 6 structural archetypes (feedback loops, layered emergence, tension resolution, resonant transfer, boundary permeability, compression-expansion)
3. Strategy Forging -- Generate new reasoning strategies from discovered patterns
4. Application -- Apply forged strategies to novel problems
5. Comparison -- Before/after metrics showing strategy impact

Forged strategy types: Resonant Tension Cycling, Compression-Resonance Bridging, Emergent Boundary Walking, Temporal Depth Stacking.

Implementation

• Base Model: Meta-Llama-3.1-8B-Instruct
• Adaptation: 9 LoRA adapters (Newton, DaVinci, Empathy, Philosophy, Quantum, Consciousness, Multi-Perspective, Systems Architecture, Orchestrator)
• Memory: SQLite + FTS5 full-text search (UnifiedMemory)
• Hardware: Validated on consumer hardware (Intel Core Ultra 7, 16GB RAM) and cloud (NVIDIA A10G)

Related Resources

Resource	Link
GitHub (Full Codebase)	Raiff1982/Codette-Reasoning
Base Model (GGUF)	Raiff1982/codette-llama-3.1-8b-gguf
LoRA Adapters	Raiff1982/codette-lora-adapters
Training Data	Raiff1982/codette-training-data
Live Demo	Raiff1982/Codette-Demo
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

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.