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

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: RC+ξ (Recursive Convergence + Epistemic Tension) — a cognitive dynamical system formalism modeling state evolution as a constrained system converging toward stable attractors Multi-Agent Reasoning Forge — consensus-based synchronization of heterogeneous cognitive agents through shared attractor dynamics AEGIS Ethical Governance — a reinforcement-aligned ethical regulator with recursive anchor feedback Key Results: 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 The framework is implemented as a six-layer modular architecture integrating eleven cognitive perspectives, a five-dimensional QuantumSpiderweb cognitive graph, persistent memory cocoons, and a parameter-efficient adapter training pipeline using LoRA/PEFT on consumer-grade hardware — including two novel GPU-free CPU training pipelines validated on commodity laptops. Base model: Meta-Llama-3.1-8B-Instruct with 8 QLoRA adapters (4-bit, rank 16, alpha 32), trained on 20,500 perspective-tagged examples across 8 cognitive domains.