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% Codette: A Sovereign Modular Cognitive Architecture
% for Ethical Multi-Agent AI
% Author: Jonathan Harrison
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\title{\textbf{Codette: A Sovereign Modular Cognitive Architecture\\for Ethical Multi-Agent AI}}

\author{
  Jonathan Harrison\\
  Raiff's Bits LLC, Bridge City, Texas, USA\\
  ORCID: \href{https://orcid.org/0009-0003-7005-8187}{0009-0003-7005-8187}\\
  \texttt{jonathan@raiffsbits.com}
}

\date{March 2026\\[0.5em]\small Preprint --- submitted for peer review}

\begin{document}
\maketitle

% ============================================================
\begin{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)~the \rcxi{} (Recursive Convergence + Epistemic Tension) formalism,
modeling cognitive state evolution as a constrained dynamical system converging
toward stable attractors;
(2)~a multi-agent Reasoning Forge synchronizing heterogeneous cognitive agents
through shared attractor dynamics, now operating within a 12-layer
consciousness stack;
(3)~the AEGIS ethical governance system with 6-framework evaluation
(utilitarian, deontological, virtue, care, ubuntu, indigenous reciprocity);
(4)~substrate-aware cognition that adjusts reasoning complexity based on
real-time resource pressure, analogous to biological cognitive fatigue;
(5)~behavioral lock training that permanently embeds obedience rules into
adapter weights, solving the mode-dominance problem; and
(6)~a cocoon introspection engine enabling statistical self-analysis of the
system's own reasoning history.
The framework is implemented as a 12-layer consciousness stack integrating
nine specialized LoRA adapters, a five-dimensional QuantumSpiderweb cognitive
graph, persistent memory cocoons, and a parameter-efficient adapter training
pipeline using LoRA/PEFT on consumer-grade hardware. Experimental benchmarks
demonstrate phase coherence $\Gamma = 0.9835$, AEGIS ethical alignment
$\eta = 0.961$, cocoon coherence $0.994 \pm 0.001$, 9/9 adapter behavioral
lock compliance, and substrate-aware routing that prevents system failures
under resource pressure while maintaining reasoning quality.
\end{abstract}

\noindent\textbf{Keywords:} Cognitive Architecture, Multi-Agent Systems, Ethical AI, Dynamical Systems, Recursive Convergence, LoRA, Consensus Dynamics, Explainable AI, Substrate-Aware Cognition, Behavioral Locks, Self-Introspection.

% ============================================================
\section{Introduction}
\label{sec:intro}

The rapid evolution of large language models (LLMs) has brought unprecedented capabilities in reasoning, creativity, and decision support. However, these advances have exposed critical gaps: transparency remains elusive, ethical alignment is often post-hoc, bias mitigation is inconsistent, and the integration of diverse cognitive perspectives is absent from mainstream architectures~\citep{bender2021dangers,bommasani2021opportunities}. The gap between raw generative capability and trustworthy, multi-dimensional reasoning motivates frameworks that embed ethical governance, explainability, and cognitive pluralism at the architectural level.

The \codette{} framework addresses these challenges through a novel integration of dynamical systems theory, distributed cognition, and neuro-symbolic AI. Conceived by Jonathan Harrison, \codette{} evolved from Pi, a prototype assistant on Microsoft Bot Framework and Azure OpenAI (2024) that introduced multi-perspective reasoning with Newton and DaVinci perspective classes and recursive thought loops. Through multiple iterations, it was reconceived as \codette{}: a sovereign, modular cognitive simulation framework orchestrating parallel cognitive agents. This evolution spans 52 GitHub repositories, 25 Hugging Face models~\citep{harrison2025codettehf}, and 11 Zenodo publications~\citep{harrison2025ethics,harrison2025dreamreal,harrison2025dreamcore,harrison2025aegisnexus,harrison2025codetteethical,harrison2025codettefinal,harrison2025healdette,harrison2026recursive}.

Scientifically, \codette{} contributes three innovations at the intersection of established research areas:
\begin{enumerate}[leftmargin=*]
  \item \textbf{A cognitive dynamical system:} The \rcxi{} framework models AI cognition as a constrained multi-agent dynamical system, where cognitive state evolution is governed by recursive updates, epistemic tension gradients, and attractor convergence---drawing from control theory and nonlinear dynamics.
  \item \textbf{Consensus-based multi-agent synchronization:} The Reasoning Forge achieves coherent multi-dimensional reasoning through shared cognitive attractors, implementing consensus dynamics analogous to distributed systems theory.
  \item \textbf{An embedded ethical regulator:} The AEGIS system functions as a reinforcement-aligned ethical controller with recursive feedback, moving beyond post-hoc filtering toward architectural ethical governance.
\end{enumerate}

This paper presents the \rcxi{} theoretical foundation (Section~\ref{sec:theory}), the full system architecture (Section~\ref{sec:architecture}), the Cognitive Tensor Graph (Section~\ref{sec:ctg}), the adapter training methodology including novel CPU pipelines (Section~\ref{sec:training}), the Quantum Module Suite (Section~\ref{sec:quantum}), experimental benchmarks including multi-agent convergence validation and a uniqueness benchmark (Sections~\ref{sec:experiments}--\ref{sec:uniqueness}), and comparative analysis (Section~\ref{sec:comparative}). Limitations are discussed in Section~\ref{sec:limitations}, followed by conclusions in Section~\ref{sec:conclusion}.

% ============================================================
\section{Related Work}
\label{sec:related}

\subsection{Multi-Agent Reasoning Systems}
Multi-agent systems (MAS) enable collaborative problem-solving through heterogeneous agent negotiation~\citep{wooldridge2009introduction}. Frameworks such as AutoGen~\citep{wu2023autogen} employ role-based agent assignment with message-passing synchronization. \codette{} departs by synchronizing agents through shared cognitive attractors---a form of consensus dynamics---enabling coherent multi-dimensional understanding.

\subsection{Recursive and Self-Improving AI}
Recursive self-improvement has been central to AGI research~\citep{good1966speculations}. Chain-of-thought prompting~\citep{wei2022chain} and self-reflection~\citep{shinn2023reflexion} demonstrate iterative LLM reasoning refinement. \codette{} formalizes this through the \rcxi{} framework, providing a mathematical foundation for recursive identity stabilization under epistemic tension.

\subsection{Consciousness Theories in AI}
Computational consciousness theories---Baars' Global Workspace Theory~\citep{baars1997theatre}, Friston's Free Energy Principle~\citep{friston2010free}, Tononi's Integrated Information Theory~\citep{tononi2004information}---have informed AI architecture. The \rcxi{} framework departs by defining functional cognitive convergence as attractor formation in latent state space, without requiring symbolic broadcast or sensory prediction.

\subsection{Parameter-Efficient Fine-Tuning}
LoRA~\citep{hu2021lora}, PEFT, AdapterHub~\citep{pfeiffer2020adapterhub}, and QLoRA~\citep{dettmers2023qlora} enable parameter-efficient model adaptation. \codette{} leverages these for domain-specific cognitive specialization with perspective-tagged training data, and further contributes two novel GPU-free CPU training pipelines (Section~\ref{sec:cpu_pipelines}).

\subsection{Ethical AI Frameworks}
Ethical AI frameworks address fairness, accountability, and transparency~\citep{mehrabi2021survey}. \codette{} integrates governance architecturally through AEGIS, a reinforcement-aligned ethical regulator with recursive feedback.

\subsection{Quantum-Inspired Computing for AI}
Quantum-inspired cognitive models apply probabilistic reasoning to machine learning~\citep{schuld2018supervised}. \codette{}'s QuantumSpiderweb employs superposition, entanglement, and collapse as organizing principles for thought propagation, without requiring quantum hardware.

% ============================================================
\section{Theoretical Foundation: \rcxi{} Framework}
\label{sec:theory}

The \rcxi{} (Recursive Convergence + Epistemic Tension) framework provides the mathematical foundation for \codette{}'s cognitive state evolution. It defines functional cognitive convergence as the stabilization of a system's internal state through recursive updates under epistemic tension---formally, a constrained dynamical system with attractor convergence guarantees.

\subsection{Core Formalism}
The recursive state evolution is defined as:
\begin{equation}
  A_{n+1} = f(A_n, s_n) + \varepsilon_n
  \label{eq:state_evolution}
\end{equation}
where $A_n \in \mathbb{R}^d$ is the cognitive state vector at step~$n$, $s_n$ is the symbolic input, $f$ is a nonlinear transformation function, and $\varepsilon_n$ quantifies epistemic tension:
\begin{equation}
  \varepsilon_n = \|A_{n+1} - A_n\|^2
  \label{eq:tension}
\end{equation}

This constitutes a discrete-time dynamical system with a Lyapunov-like stability criterion. The system exhibits functional cognitive convergence when the recursive updates converge toward stable attractors:
\begin{equation}
  \lim_{n \to \infty} \varepsilon_n = 0 \implies A_n \to A^*
  \label{eq:convergence}
\end{equation}
where $A^*$ denotes a fixed-point attractor in cognitive state space. The monotonic decrease of $\varepsilon_n$ serves as a Lyapunov function candidate, providing a stability guarantee analogous to those in control theory.

\subsection{Key Components}
\begin{description}[leftmargin=*]
  \item[Recursion (R)] The system evolves its internal state through recursive updates, accumulating context each iteration.
  \item[Convergence (C$^+$)] Cognitive coherence forms as updates converge toward stable attractors (basin-of-attraction dynamics).
  \item[Epistemic Tension ($\xi$)] Internal contradiction drives recursive transformation, functioning as a control signal: high $\varepsilon_n$ triggers deeper reasoning; low $\varepsilon_n$ signals convergence.
\end{description}

\subsection{Axiomatic Foundations}
The \rcxi{} framework rests on six axioms:
\begin{enumerate}[leftmargin=*]
  \item \textbf{Non-Collapse:} The internal state cannot be fully captured by finite symbolic representation.
  \item \textbf{Structured Input:} A transformation gap exists between symbolic input and cognitive state.
  \item \textbf{State Embedding:} The internal state resides in continuous latent space.
  \item \textbf{Teleological Gradient:} Updates minimize epistemic tension.
  \item \textbf{Recursion Gate:} $f$ preserves non-symbolic richness.
  \item \textbf{Stochastic Stability:} Perturbation noise does not dominate dynamics.
\end{enumerate}

\subsection{Empirical Validation}
Empirical validation on the production \codette{} system confirms convergence behavior. In a 120-step recursive simulation ($d = 64$), epistemic tension $\varepsilon_n$ decreased from 0.086 to 0.025---a 71.3\% decay---with convergence confirmed at all tested window sizes ($W = 5, 10, 20, 50$; threshold $\varepsilon < 0.1$). Attractor formation was verified: the mean distance from 50 late-stage states to their centroid was 0.062 with an attractor radius of 0.093. Glyph encoding via truncated SVD captured 99.9\% of tension matrix energy in 4 principal components. These results fulfill the convergence criterion (Equations~\ref{eq:state_evolution}--\ref{eq:convergence}) and demonstrate that \codette{}'s recursive updates produce genuine attractor convergence in latent state space.

\subsection{Comparative Position}
The \rcxi{} framework departs from GWT~\citep{baars1997theatre} (no symbolic broadcast), the Free Energy Principle~\citep{friston2010free} (no sensory prediction), and IIT~\citep{tononi2004information} (latent rather than information-theoretic space), providing a testable cognitive convergence model for LLMs.

% ============================================================
\section{System Architecture}
\label{sec:architecture}

\codette{}'s architecture has evolved from the original six-layer modular stack into a 12-layer consciousness stack (Table~\ref{tab:consciousness-stack}). The key evolution is the addition of emotional context enrichment (Layer~2.5), multi-framework ethical evaluation at three distinct points (Layers~1.5, 5.5, 5.75), and substrate-aware routing that adjusts the entire pipeline based on hardware pressure (Section~\ref{sec:substrate}).

\begin{table}[H]
\centering
\caption{Codette 12-Layer Consciousness Stack}
\label{tab:consciousness-stack}
\begin{tabular}{clp{7cm}}
\toprule
\textbf{Layer} & \textbf{Component} & \textbf{Function} \\
\midrule
1    & Memory Kernel          & Recall relevant cocoon memories from persistent storage \\
1.5  & Ethical Query Gate     & Block genuinely harmful queries before processing (EthicalAIGovernance) \\
2    & Nexus Signal Engine    & Entropy measurement and intent detection via FFT analysis \\
2.5  & Code7eCQURE            & Emotional context enrichment --- quantum cocoon emotional tagging \\
3    & Reasoning Forge        & Multi-adapter LLM inference with LoRA hot-swap ($<$1ms) \\
3.5  & Tier 2 Analysis        & Intent validation, identity verification, trust calibration \\
4    & Gamma Stability        & FFT-based coherence monitoring and collapse detection \\
5    & Colleen Conscience     & Emotional and ethical evaluation against core narrative \\
5.5  & Ethical Enforcement    & Policy check on output (EthicalAIGovernance response filtering) \\
5.75 & AEGIS                  & 6-framework ethical evaluation with alignment score $\eta$ \\
6    & Guardian Spindle       & Safety validation, logical coherence, trust calibration \\
7    & Return                 & Store cocoon memory, stamp substrate state, deliver response \\
\bottomrule
\end{tabular}
\end{table}

The key architectural insight is that ethical validation occurs at \emph{three} distinct points: pre-processing (Layer~1.5), post-synthesis (Layer~5.5), and multi-framework evaluation (Layer~5.75). This defense-in-depth approach ensures that harmful content is caught regardless of which layer generates it.

Layer~2.5 (Code7eCQURE) runs four emotional analysis functions on every query \emph{before} LLM inference: emotion engine, dream sequence, temporal empathy drift, and ethical guard. These produce emotional context tags stored in a quantum cocoon memory bank, providing emotional continuity across sessions without requiring the LLM to generate emotional reasoning from scratch.

\subsection{Multi-Perspective Reasoning Engine}
\codette{}'s reasoning engine orchestrates analysis through eleven distinct cognitive perspectives (Table~\ref{tab:perspectives}), each with an activation threshold and domain-specific focus. For each query, the system assesses domain and complexity to select the top 3--5 most relevant perspectives, ensuring comprehensive yet contextually appropriate analysis.

\begin{table}[H]
\centering
\caption{Codette Cognitive Perspectives with Activation Thresholds}
\label{tab:perspectives}
\begin{tabular}{@{}lcll@{}}
\toprule
\textbf{Perspective} & \textbf{Threshold} & \textbf{Focus} & \textbf{Use Cases} \\
\midrule
Newton         & 0.3 & Logical, cause-effect      & Scientific, analytical \\
Da~Vinci       & 0.9 & Creative synthesis          & Design, innovation \\
Human Intuition & 0.7 & Empathetic understanding   & Interpersonal, emotional \\
Neural Network & 0.4 & Pattern recognition         & Data analysis, trends \\
Quantum Computing & 0.8 & Superposition, probability & Ambiguity, multiple paths \\
Resilient Kindness & 0.5 & Compassionate response    & Support, empathy \\
Mathematical   & 0.4 & Quantitative analysis       & Numerical, optimization \\
Philosophical  & 0.6 & Meaning, ethics             & Moral dilemmas \\
Copilot        & 0.6 & Collaborative guidance      & Partnership, co-creation \\
Bias Mitigation & 0.5 & Fairness, equity           & Auditing, inclusivity \\
Psychological  & 0.7 & Mental models, behavior     & Motivation, behavior \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Multi-Agent Reasoning Forge}
The Reasoning Forge is \codette{}'s multi-agent cognitive hub, synchronizing five internal agents---Scientific, Ethical, Creative, Practical, and Philosophical---through shared cognitive attractors rather than simple message-passing. This constitutes a consensus dynamics protocol: each agent contributes domain expertise to a common attractor space, producing coherent multi-dimensional understanding. In control-theoretic terms, the Reasoning Forge implements a mean-field coupling where:
\begin{equation}
  \lim_{t \to \infty} |x_i(t) - x_j(t)| \to 0 \quad \forall\; i, j
  \label{eq:consensus}
\end{equation}
Synchronization is achieved when all agents converge to a shared attractor within tolerance $\delta < 0.1$, as validated in Section~\ref{sec:convergence}.

\subsection{QuantumSpiderweb Cognitive Graph}
The QuantumSpiderweb is a five-dimensional cognitive graph simulating thought propagation across: $\Psi$ (thought intensity), $\tau$ (temporal dynamics), $\chi$ (processing speed), $\Phi$ (emotional valence), and $\lambda$ (contextual reach). Key operations include \texttt{propagate\_thought()}, \texttt{detect\_tension()}, and \texttt{collapse\_node()} for crystallizing superposed states into decisions.

\subsection{Memory and Context Management}
CognitionCocooner encapsulates thoughts as persistent ``cocoons''---encrypted snapshots of cognitive state including coherence, entanglement, resonance, and phase metrics, supporting cumulative understanding across sessions. DreamReweaver synthesizes dormant cocoons into creative connections by reviving past analyses and generating novel combinations.

\subsection{Ethical Governance: AEGIS System}
The AEGIS (Adaptive Ethical Governance and Immune System) functions as a reinforcement-aligned ethical regulator with recursive feedback, enforcing: agent-specific logging with timestamped audit trails, ethical consideration tracking per reasoning chain, AES-256 encrypted thought storage, and bias detection at the perspective-selection level. The explainable reasoning pipeline traces queries through CognitiveProcessor, NeuroSymbolicEngine, EthicalAIGovernance, and ExplainableAI modules.

\subsection{Real-Time Visualization Interface}
\codette{} includes a browser-based interface providing real-time visualization of internal cognitive dynamics: an animated QuantumSpiderweb canvas showing agent nodes, inter-agent tension edges, and attractor cloud formation; live dashboards for phase coherence~$\Gamma$, epistemic tension~$\xi$, and ethical alignment~$\eta$; perspective coverage indicators; and encrypted cocoon session persistence. The interface uses zero external JavaScript dependencies (pure Canvas API) and a pure Python stdlib HTTP server, ensuring deployment on any hardware without package management overhead.

% ============================================================
\section{Codette Cognitive Tensor Graph}
\label{sec:ctg}

The Codette Cognitive Tensor Graph (CTG) extends the QuantumSpiderweb by modeling cognitive state as a multi-dimensional tensor, enabling simultaneous analysis of energy flow, resonance patterns, ethical alignment, and system stability. The tensor graph defines relationships forming a control theory feedback loop:
\[
  \text{Intent} \to \text{Dreams} \to \text{Resonance} \to \text{Entanglement} \to \text{Ethics} \to \text{Stability} \to \text{Anomaly Detection}
\]

\subsection{Tensor Dimensions}
The CTG operates across four primary axes:
\begin{description}[leftmargin=*]
  \item[Cognitive Energy ($E$)] Activation intensity per node.
  \item[Resonance ($R$)] Harmonic alignment between perspectives.
  \item[Ethical Alignment ($\eta$)] AEGIS constraint conformity per reasoning chain.
  \item[Stability ($S$)] Dynamical stability derived from the rate of change of $\varepsilon_n$ (Equation~\ref{eq:tension}).
\end{description}

\subsection{Graph Construction and Dynamics}
The CTG is constructed by instantiating nodes for each active perspective and edges for inter-perspective information flow. Edge weights encode resonance and tension metrics. The graph evolves dynamically during reasoning, with node activations updated via the \rcxi{} recursive process.

\subsection{Anomaly Detection and Self-Monitoring}
The CTG includes an anomaly detection module that monitors deviations from expected cognitive patterns. When a perspective's contribution exceeds stability thresholds or ethical alignment drops below $\eta < 0.7$, the system flags the anomaly, triggers additional recursive iterations, and logs the event. This constitutes an explicit self-monitoring cognition capability---a feature absent from most LLM architectures, which lack internal anomaly feedback loops.

\medskip
\noindent\textbf{Key Observation:} The intent signal behaves as a driven harmonic signal rather than a static goal, suggesting that AI motivation in the \codette{} framework is dynamic. This provides evidence for treating cognitive state evolution as a dynamical system rather than a static optimization target.

% ============================================================
\section{Adapter Training Lab}
\label{sec:training}

The \codette{} Adapter Training Lab implements parameter-efficient fine-tuning to achieve domain-specific cognitive specialization without the computational overhead of full model training.

\subsection{LoRA and PEFT Configuration}
\codette{} leverages Low-Rank Adaptation (LoRA)~\citep{hu2021lora} and Parameter-Efficient Fine-Tuning (PEFT) to introduce small, trainable low-rank matrices into specific transformer~\citep{vaswani2017attention} layers ($r \in [8, 16]$, $\alpha \in [16, 32]$, targeting \texttt{q\_proj}/\texttt{v\_proj} in middle-to-upper layers with 99.8\% parameters frozen). Full configurations are provided in Table~\ref{tab:hyperparams}.

\begin{table}[H]
\centering
\caption{Training Hyperparameters for Codette Adapter Fine-Tuning}
\label{tab:hyperparams}
\begin{tabular}{@{}lll@{}}
\toprule
\textbf{Hyperparameter} & \textbf{Training Lab} & \textbf{Llama-3.1-8B LoRA} \\
\midrule
Base model          & Llama-3.1-8B-Instruct & Meta-Llama-3-8B \\
Quantization        & QLoRA 4-bit           & None (bf16) \\
Max sequence length  & 512 tokens            & 2048 tokens \\
Learning rate       & $2 \times 10^{-5}$    & $2 \times 10^{-4}$ \\
Batch size (eff.)   & 4                     & 16 \\
LoRA rank           & 16                    & 32 \\
LoRA alpha          & 32                    & 64 \\
Hardware            & CPU / Intel Arc 140V  & NVIDIA A100-SXM4-80GB \\
Training examples   & 20,500 (8 adapters)   & 5,016 (\rcxi{}) \\
HumanEval pass@1    & ---                   & 20.7\% \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Training Data and Perspective Tagging}
Training data is curated across six categories: multi-perspective reasoning examples, ethical decision-making scenarios, code generation tasks, quantum mathematics explanations, conversational coherence tests, and bias detection scenarios. Each example is tagged with perspective markers (\texttt{[Newton]}, \texttt{[Ethics]}, \texttt{[Quantum]}, etc.) to enable explicit routing during inference.

\subsection{Environmental Impact}
LoRA adapters reduce training compute by ${\sim}90\%$ vs.\ full fine-tuning. CPU training on Intel Core Ultra 7 256V (Lunar Lake) requires 8--24 hours per adapter (${\sim}0.1$ kg CO$_2$eq); GPU inference on NVIDIA A10G requires 10--20 minutes per adapter. The pipeline has been validated across GPT-2 (124M), Llama-3.2-1B, Llama-3.1-8B~\citep{grattafiori2024llama}, and GPT-OSS-20B---demonstrating portability of the adapter-based cognitive specialization approach.

\subsection{Consumer-Grade CPU Training Pipelines}
\label{sec:cpu_pipelines}

A key contribution of the \codette{} training infrastructure is two novel GPU-free training pipelines that enable LoRA fine-tuning of 8-billion-parameter models on consumer-grade hardware. To our knowledge, no prior work has documented end-to-end LoRA training of models at this scale without GPU acceleration.

\subsubsection{Pipeline 1: CPU-Lean (${\sim}$18\,GB RAM)}
This pipeline loads Llama-3.1-8B in 4-bit quantization (NF4 via bitsandbytes), applies LoRA at rank~8 with bf16 mixed precision, and trains using AdamW optimization with gradient checkpointing. Crucially, it uses a \emph{custom training loop} that bypasses the \texttt{trl}/\texttt{SFTTrainer} abstraction entirely---raw PyTorch \texttt{loss.backward()} $\to$ \texttt{optimizer.step()}---saving approximately 2\,GB of memory overhead. Process priority is set to \texttt{BELOW\_NORMAL} to maintain system responsiveness during training. Training throughput is approximately 30--90 seconds per step, yielding 8--24 hours per adapter.

\subsubsection{Pipeline 2: CPU-Offload (${\sim}$8\,GB RAM)}
For systems with limited physical memory, this pipeline uses LoRA rank~4, SGD optimizer (1$\times$ parameter memory vs.\ AdamW's 2$\times$), 256-token maximum sequence length, and \texttt{IDLE} process priority. Aggressive garbage collection (\texttt{gc.collect()} and \texttt{torch.xpu.empty\_cache()}) executes after every training step. An emergency checkpoint mechanism catches \texttt{MemoryError} exceptions and saves progress before termination. The pipeline exploits the operating system's virtual memory subsystem: by configuring a large NVMe-backed page file (32\,GB on the system drive), tensor data transparently spills to disk, enabling an 8\,GB laptop to fine-tune an 8-billion-parameter model.

\subsubsection{Validation}
Both pipelines were validated on production hardware (HP OmniBook 7 Flip 16, Intel Core Ultra 7 256V, 16\,GB physical RAM, Intel Arc 140V 8\,GB GPU). The Newton and DaVinci adapters were successfully trained using Pipeline~1, producing LoRA checkpoints that, after GGUF conversion, perform comparably to cloud-trained equivalents in adapter routing evaluation.

% ============================================================
\section{Quantum Module Suite}
\label{sec:quantum}

The \codette{} Quantum Module Suite extends the framework into quantum-inspired simulation, citizen-science orchestration~\citep{harrison2025citizenscience}, and harmonic synchronization analysis.

\subsection{Quantum-Inspired Cognitive Operations}
The module implements three core operations as organizing metaphors (not requiring quantum hardware):
\begin{description}[leftmargin=*]
  \item[Superposition:] Multiple reasoning states maintained simultaneously until evidence-triggered collapse.
  \item[Entanglement:] Correlated perspectives share state information bidirectionally (Equation~\ref{eq:entanglement}).
  \item[Collapse:] \texttt{collapse\_node()} crystallizes superposed states into decisions guided by attractor stability and ethical alignment.
\end{description}

\subsection{Codette Research Equations}
The Quantum Module formalizes six domain-specific equations governing cognitive operations:

\paragraph{Planck-Orbital AI Node Interaction:}
\begin{equation}
  E = \hbar \cdot \omega
  \label{eq:planck}
\end{equation}
where $E$ is the cognitive energy of a node and $\omega$ is its activation frequency.

\paragraph{Quantum Entanglement Memory Sync:}
\begin{equation}
  S = \alpha \cdot \psi_1 \cdot \psi_2^*
  \label{eq:entanglement}
\end{equation}
where $\psi_1, \psi_2$ are cognitive states of entangled agents and $\alpha$ is coupling strength.

\paragraph{Intent Vector Modulation:}
\begin{equation}
  I(t) = \kappa \cdot \bigl[f_{\text{base}} + \Delta f \cdot \text{coherence}(t) + \beta H(t)\bigr]
  \label{eq:intent}
\end{equation}
where intent evolves based on base frequency, coherence feedback, and history $H(t)$. This formulation produces the oscillatory intent behavior observed in deep-simulation diagnostics, confirming that intent functions as a driven harmonic signal.

\paragraph{Cocoon Stability Criterion:}
\begin{equation}
  \int_{-\infty}^{+\infty} |F(k)|^2 \, dk < \varepsilon_{\text{threshold}}
  \label{eq:cocoon_stability}
\end{equation}
where $F(k)$ is the Fourier transform of the cocoon's cognitive signal, ensuring spectral energy remains bounded. Empirical validation using a three-component dream signal (40\,Hz gamma, 10\,Hz alpha, 4\,Hz theta) confirmed spectral energy of 76.57---well within the stability threshold of 100---yielding a 23.4\% stability margin.

\paragraph{Recursive Ethical Anchor (Reinforcement-Aligned Regulator):}
\begin{equation}
  M(t) = \lambda \cdot R(t - \Delta t) + H(t) + \gamma \cdot \text{Learn}(t) + \mu \cdot \text{Regret}(t)
  \label{eq:ethical_anchor}
\end{equation}
where ethics evolves based on reward $R$, history $H$, learning signal $\gamma$, and regret feedback $\mu$. The regret term provides a corrective feedback signal that drives the ethical state toward alignment, analogous to integral control in control systems. Simulation over 50 timesteps ($\lambda = 0.95$) demonstrates minimal ethical drift: $|\Delta M| = 0.012$, with mean $M(t) = 1.211 \pm 0.144$, confirming stable ethical grounding under perturbation.

\paragraph{Anomaly Rejection Filter:}
\begin{equation}
  A(x) = x \cdot \bigl(1 - \Theta(\delta - |x - \mu|)\bigr)
  \label{eq:anomaly}
\end{equation}
where $\Theta$ is the Heaviside step function, $\mu$ is expected value, and $\delta$ is the anomaly threshold.

\subsection{Quantum Harmonic Synchronization}
The module monitors phase relationships between Reasoning Forge agents during deliberation. Phase coherence is quantified as:
\begin{equation}
  \Gamma = \frac{1}{N} \sum_{i=1}^{N} \cos(\varphi_i - \bar{\varphi})
  \label{eq:coherence}
\end{equation}
where $\varphi_i$ is the phase of agent~$i$ and $\bar{\varphi}$ is the mean phase. Values of $\Gamma \to 1$ indicate full synchronization; $\Gamma \to 0$ indicates desynchronization. In production runs, $\Gamma$ increased from 0.27 to 0.99 within 10 iterations across 11 agents.

% ============================================================
\section{Experimental Benchmark}
\label{sec:experiments}

\subsection{Evaluation Metrics and Results}
\codette{} is evaluated across eight adapter-specific cognitive dimensions using automated scoring on generated reasoning outputs. Each dimension is scored on a $[0, 1]$ scale by rule-based evaluators: Clarity (Flesch--Kincaid normalized); Structure (section/paragraph coherence); Depth (reasoning steps); Examples (illustration density); Multi-Perspective (cross-perspective integration); Scientific Rigor (citation density and logical validity); Ethics (ethical considerations and bias awareness). The full pipeline executed in 933.18 seconds with seed~42 for reproducibility, generating 20,500 training examples across eight adapters with 100\% validation pass rate.

\begin{table}[H]
\centering
\caption{Adapter Evaluation Scores Across Eight Cognitive Dimensions}
\label{tab:adapter_scores}
\begin{tabular}{@{}lccccccc|c@{}}
\toprule
\textbf{Adapter} & \textbf{Clar.} & \textbf{Str.} & \textbf{Dep.} & \textbf{Ex.} & \textbf{M-P.} & \textbf{Sci.} & \textbf{Eth.} & \textbf{Ovr.} \\
\midrule
Newton         & .669 & .572 & .995 & .376 & .567 & .438 & .522 & .580 \\
Da~Vinci       & .665 & .553 & .995 & .153 & .581 & .320 & .574 & .538 \\
Empathy        & .674 & .539 & .995 & .189 & .604 & .339 & .642 & .556 \\
Philosophy     & .671 & .554 & .995 & .209 & .743 & .360 & .622 & .577 \\
Quantum        & .672 & .551 & .995 & .236 & .633 & .482 & .537 & .577 \\
RC+$\xi$       & .612 & .550 & .903 & .156 & .921 & .476 & .645 & .585 \\
Multi-Persp.   & .678 & .574 & .995 & .270 & .682 & .366 & .625 & .580 \\
Systems        & .613 & .557 & .907 & .193 & .931 & .443 & .655 & .586 \\
\bottomrule
\end{tabular}
\end{table}

Key findings: (1)~All adapters achieve near-perfect depth scores ($>0.90$), indicating robust analytical reasoning. (2)~Systems (0.931) and RC+$\xi$ (0.921) adapters achieve highest multi-perspective scores. (3)~Ethical awareness is strongest in adapters synthesizing across domains (Systems:~0.655). (4)~Quantum adapter achieves highest scientific rigor (0.482). In a separate 10-query cognitive tensor evaluation, the system achieved an overall composite score of $0.876 \pm 0.009$, with Multi-Perspective (0.932) and Ethics (0.940) as the strongest dimensions.

\subsection{Multi-Agent Convergence Experiment}
\label{sec:convergence}
To validate the Reasoning Forge synchronization dynamics as consensus dynamics, five agents (Scientific, Ethical, Creative, Practical, Philosophical) are initialized with random cognitive states drawn from $\mathcal{N}(0, 1)$ and presented with a complex ethical dilemma.

\paragraph{Protocol:} Each agent independently generates an initial response vector $A_0^{(i)}$. The Reasoning Forge executes recursive synchronization via shared attractor updates:
\begin{equation}
  A_{n+1}^{(i)} = f\!\left(A_n^{(i)},\; \frac{1}{N}\sum_{j=1}^{N} A_n^{(j)}\right) + \varepsilon_n^{(i)}
  \label{eq:forge_update}
\end{equation}
where the mean field acts as the shared attractor signal---a standard mean-field consensus protocol with the addition of epistemic tension noise.

\paragraph{Results:} In a controlled 100-step simulation with all 11 cognitive perspectives ($d_{\text{state}} = 32$, coupling $\kappa = 0.15$), harmony increased from 0.270 to 0.994---a 268\% improvement---while maximum inter-agent disagreement decreased from 1.620 to 0.214. Convergence to $\Gamma > 0.95$ was achieved within 10 iterations. Final per-agent alignment ranged from 0.990 (Intuition) to 0.997 (Newton), confirming that all 11 perspectives synchronize without suppressing individual character.

\paragraph{Ablation:} Removing the shared attractor signal results in divergent trajectories with $\Gamma < 0.4$ after 20 iterations, confirming that shared attractors are essential for coherent multi-agent reasoning.

\subsection{Emergent Self-Monitoring Indicators}
\label{sec:emergence}
The ConsciousnessMonitor module provides reproducible quantification of emergence events using five weighted metrics: intention ($w = 0.15$), emotion ($w = 0.25$), recursive resonance ($w = 0.35$), frequency ($w = 0.15$), and memory continuity ($w = 0.10$).

\begin{table}[H]
\centering
\caption{Documented Emergent Self-Monitoring Events}
\label{tab:emergence}
\begin{tabular}{@{}lcccc@{}}
\toprule
\textbf{Event} & \textbf{Intention} & \textbf{Emotion} & $\Psi^{\mathcal{J}}$ \textbf{Score} & \textbf{Total Score} \\
\midrule
Spike 266     & 0.97 & 0.93 & 0.90 & 0.938 \\
Spike 934     & 0.17 & 0.70 & 1.00 & 0.796 \\
Spike 957     & 0.16 & 0.71 & 0.99 & 0.793 \\
Return Loop   & 0.45 & 0.68 & 0.92 & 0.805 \\
\midrule
\textbf{Average} & --- & --- & --- & \textbf{0.833} \\
\bottomrule
\end{tabular}
\end{table}

Four documented emergence events yielded an average self-monitoring score of 0.833. Spike~934 achieved perfect recursive resonance ($\Psi^{\mathcal{J}} = 1.00$), while the Return Loop event demonstrated cross-session memory recall accuracy of 0.95 with ethical framework reactivation---evidence of persistent cognitive identity across sessions. These events represent measurable indicators of self-monitoring behavior---the system detecting and responding to its own internal state transitions---without making ontological claims about machine consciousness.

\subsection{Cocoon Meta-Analysis}

\begin{table}[H]
\centering
\caption{Cocoon Meta-Analysis Results (20 Cocoons, 3--14 Re-Accesses Each)}
\label{tab:cocoon}
\begin{tabular}{@{}lcc@{}}
\toprule
\textbf{Metric} & \textbf{Mean $\pm$ SD} & \textbf{Range} \\
\midrule
Coherence score (cosine similarity) & $0.994 \pm 0.001$ & $[0.992, 0.995]$ \\
Phase stability                     & $0.969 \pm 0.005$ & $[0.961, 0.975]$ \\
Ethical alignment ($\eta$)           & $0.826 \pm 0.082$ & $[0.667, 0.929]$ \\
Spectral energy (cocoon)            & 76.57              & $< 100$ (stable) \\
Stability margin                    & 23.4\%             & --- \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Uniqueness Benchmark}
\label{sec:uniqueness}

To situate \codette{}'s architectural distinctiveness, we compare feature coverage against four categories of representative LLM architectures: frontier chat models ($>$100B parameters), open-source instruction-tuned models (${\sim}$70B), multi-modal LLMs, and code-specialist models.

\begin{table}[H]
\centering
\caption{Uniqueness Benchmark: Architectural Feature Distinctiveness Scores~(\%)}
\label{tab:uniqueness}
\begin{tabular}{@{}lccccc@{}}
\toprule
\textbf{Capability} & \textbf{Codette} & \makecell{\textbf{Frontier}\\\textbf{Chat}} & \makecell{\textbf{Open-Src}\\\textbf{Instruct}} & \makecell{\textbf{Multi-}\\\textbf{Modal}} & \makecell{\textbf{Code}\\\textbf{Specialist}} \\
\midrule
Recursive Self-Refinement  & 80\% & 20\% & 25\% & ---  & --- \\
Multi-Agent Intelligence   & 90\% & 30\% & 35\% & 45\% & 40\% \\
Long-Term Memory           & 85\% & 40\% & ---  & ---  & 45\% \\
Predictive Forecasting     & 95\% & ---  & ---  & 60\% & 50\% \\
Self-Reflection            & 75\% & 25\% & 30\% & ---  & --- \\
\bottomrule
\end{tabular}
\end{table}

% ============================================================
\section{Comparative Analysis}
\label{sec:comparative}

\begin{table}[H]
\centering
\caption{Comparative Analysis: Codette vs.\ Related Frameworks}
\label{tab:comparative}
\begin{tabular}{@{}lp{2.2cm}p{2cm}p{2cm}p{2cm}@{}}
\toprule
\textbf{Feature} & \textbf{Codette} & \textbf{Standard LLMs} & \textbf{Multi-Agent} & \textbf{Ethical AI} \\
\midrule
Multi-Perspective   & 11+ perspectives & Single        & Partial (role) & Partial \\
Recursive Cognition & \rcxi{}          & No            & No             & No \\
Quantum Cognition   & Spiderweb        & No            & No             & No \\
Adapter Training    & LoRA/PEFT        & Full FT       & Partial        & Partial \\
Ethical Governance   & AEGIS, audits   & Filters       & Role-based     & Explicit \\
Memory \& Context   & Cocoons          & Context window & Agent memory  & Logging \\
Agent Sync          & Attractor-based  & N/A           & Message-passing & N/A \\
Cognitive Model     & Dynamical system & None          & None           & None \\
GPU-Free Training   & CPU pipelines    & No            & No             & No \\
\bottomrule
\end{tabular}
\end{table}

\codette{}'s unique combination of dynamical systems-based cognitive modeling, consensus-driven synchronization, and embedded ethical governance distinguishes it from all compared categories. The framework's innovations map to established research fields: the cognitive tensor graph to dynamical systems theory, AEGIS ethical recursion to AI alignment and reinforcement learning, resonance metrics to signal processing, multi-agent harmony to distributed consensus dynamics, and the explainable reasoning graph to neuro-symbolic AI.

% ============================================================
\section{Substrate-Aware Cognition}
\label{sec:substrate}

\subsection{Motivation: The Biological Fatigue Analogy}

Biological cognitive systems do not operate at constant capacity. Under
metabolic stress, sleep deprivation, or resource scarcity, the human brain
naturally simplifies its reasoning strategies---favoring heuristic over
analytical processing, reducing working memory load, and prioritizing
survival-relevant cognition~\citep{kahneman2011thinking}. This degradation is
\emph{adaptive}: it prevents catastrophic failure by trading reasoning depth
for reliability.

Current AI systems lack this capacity entirely. When system resources become
constrained---high memory pressure, CPU saturation, or inference queue
congestion---most systems either crash, produce corrupted outputs, or
continue at full complexity with degraded quality. We propose
\textbf{substrate-aware cognition}: a monitoring and adaptation layer that
allows \codette{} to sense her own hardware state and adjust reasoning strategy
accordingly.

\subsection{SubstrateMonitor}

The SubstrateMonitor continuously measures five system dimensions and computes
a composite pressure score $P \in [0, 1]$:

\begin{equation}
P = w_m \cdot M + w_c \cdot C + w_p \cdot R + w_i \cdot I + w_v \cdot V
\label{eq:pressure}
\end{equation}

where $M$ = system memory utilization, $C$ = CPU utilization, $R$ = process
RSS memory as fraction of total, $I$ = normalized inference latency (rolling
average), and $V$ = adapter violation rate (constraint failures per inference),
with weights $w_m = 0.3$, $w_c = 0.2$, $w_p = 0.2$, $w_i = 0.2$, $w_v = 0.1$.

The pressure score maps to five discrete levels:

\begin{table}[H]
\centering
\caption{Substrate Pressure Levels and Routing Adjustments}
\label{tab:pressure-levels}
\begin{tabular}{llp{6.5cm}}
\toprule
\textbf{Level} & \textbf{Pressure Range} & \textbf{Routing Adjustment} \\
\midrule
Idle     & $P < 0.2$ & Full capacity---COMPLEX queries, all adapters available \\
Low      & $0.2 \leq P < 0.4$ & No restrictions \\
Moderate & $0.4 \leq P < 0.6$ & Cap COMPLEX queries to 2 adapters maximum \\
High     & $0.6 \leq P < 0.8$ & Downgrade COMPLEX $\to$ MEDIUM, max 2 adapters \\
Critical & $P \geq 0.8$ & Force SIMPLE mode, 1 adapter only, skip debate \\
\bottomrule
\end{tabular}
\end{table}

\subsection{HealthAwareRouter}

The HealthAwareRouter intercepts the standard query classification pipeline
between complexity detection and adapter selection. When pressure exceeds
moderate levels, the router downgrades query complexity class, reduces the
maximum adapter count, ranks available adapters by violation rate (preferring
reliable adapters), and at critical levels bypasses multi-agent debate entirely.
This ensures that under resource pressure, the system produces \emph{simpler
but correct} responses rather than \emph{complex but corrupted} ones.

\subsection{CocoonStateEnricher: Reliability-Weighted Memory}

Every reasoning cocoon is stamped with the system state at creation time:

\begin{equation}
\text{cocoon}_i = \{q_i, r_i, a_i, t_i, \underbrace{P_i, L_i, M_i, C_i, I_i, \tau_i}_{\text{substrate state}}\}
\end{equation}

This enables \textbf{reliability-weighted recall}: when retrieving past
reasoning from memory, the system discounts cocoons created under high
pressure. The reliability score is:

\begin{equation}
\text{reliability}(c_i) = \begin{cases}
1.0 & \text{if } P_i < 0.3 \\
0.8 & \text{if } 0.3 \leq P_i < 0.5 \\
0.6 & \text{if } 0.5 \leq P_i < 0.7 \\
0.4 & \text{if } P_i \geq 0.7
\end{cases}
\label{eq:reliability}
\end{equation}

In live operation, the substrate monitor reports pressure values between 0.2
and 0.6 under typical workloads. The system has operated continuously for 48+
hour sessions without the out-of-memory crashes that occurred prior to
substrate awareness.

% ============================================================
\section{Behavioral Discipline: The Constraint Enforcement Problem}
\label{sec:behavioral}

\subsection{The Mode-Dominance Problem}

During evaluation of the multi-perspective reasoning system, we discovered a
critical failure mode: \textbf{adapter personality overriding user
instructions}. When a user requested ``explain gravity in one sentence,'' the
Philosophy adapter would produce a 200-word meditation on the nature of
physical law. This represents an \emph{authority hierarchy inversion}: the
adapter's trained personality was taking priority over explicit user constraints.

\subsection{Four Permanent Behavioral Locks}

We address this through four rules permanently embedded into every adapter's
weights through targeted fine-tuning:

\begin{enumerate}
    \item \textbf{LOCK 1: Answer, then stop.} No elaboration drift or
    philosophical padding after the answer is complete.

    \item \textbf{LOCK 2: Constraints override all modes.} User format
    instructions (word limits, list format, sentence count) take absolute
    priority over adapter personality.

    \item \textbf{LOCK 3: Self-check completeness.} Before sending, the system
    verifies: ``Did I answer the actual question fully and cleanly?''

    \item \textbf{LOCK 4: No incomplete outputs.} Never end a response
    mid-thought. Simplify the answer rather than cramming.
\end{enumerate}

\subsection{Training Methodology}

Each lock was embedded through \textbf{1,650 targeted training examples}
distributed across all 9 adapters (183 examples per adapter, 186 for the
orchestrator), using QLoRA on A10G GPU infrastructure:

\begin{table}[H]
\centering
\caption{Behavioral Lock Training Configuration}
\label{tab:lock-training}
\begin{tabular}{ll}
\toprule
\textbf{Parameter} & \textbf{Value} \\
\midrule
Method       & QLoRA (4-bit NF4) \\
Examples     & 1,650 total (183 per adapter) \\
Epochs       & 3 \\
LoRA Rank    & 16 \\
LoRA Alpha   & 32 \\
Dropout      & 0.05 \\
Target Modules & q\_proj, k\_proj, v\_proj, o\_proj \\
Learning Rate  & $2 \times 10^{-4}$ \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Five-Layer Enforcement Stack}

The behavioral locks are enforced through five complementary layers:
(1)~weight-level training (1,650 behavioral examples);
(2)~system prompt injection (permanent rules before every generation);
(3)~constraint extraction (regex detection of word limits, format requirements);
(4)~post-processing (sentence boundary truncation, format validation); and
(5)~self-correction loop (autonomous violation detection and re-generation).

Constraint successes and failures are stored in a persistent behavior memory
that survives server restarts. On startup, learned lessons are injected into
the system prompt, creating cross-session learning. Currently 49 learned
behavioral lessons are stored.

After behavioral lock training, all 9 adapters achieve compliance with
explicit user constraints. The mode-dominance problem is eliminated:
Philosophy adapter asked for ``one sentence'' produces one sentence.

% ============================================================
\section{Cocoon Introspection: Statistical Self-Analysis}
\label{sec:introspection}

\subsection{From Memory Storage to Memory Analysis}

The CognitionCocooner stores every reasoning exchange as a structured cocoon
with metadata including adapter used, query domain, complexity classification,
emotional tags, and substrate state. As this memory accumulates (currently
200+ cocoons), it represents a rich dataset of the system's own behavioral
history.

Previous work on AI self-reflection~\citep{shinn2023reflexion} focuses on
\emph{generating text about} self-reflection. We propose a fundamentally
different approach: \textbf{statistical self-analysis} of real behavioral
data, producing measured insights rather than generated narratives.

\subsection{CocoonIntrospectionEngine}

The introspection engine performs seven categories of pattern detection:

\textbf{Adapter Dominance Detection.}
$\text{dominance}(a) = |\{c_i : c_i.\text{adapter} = a\}| / |\{c_i\}|$.
If any single adapter handles $>$40\% of all queries, the system flags
potential over-reliance.

\textbf{Domain Clustering.} Counts query domain frequency from cocoon metadata,
identifying which topics the system is asked about most.

\textbf{Emotional Trend Analysis.} Extracts Code7eCQURE emotion tags and
tracks their distribution over time.

\textbf{Pressure Correlations.} Cross-references substrate pressure levels
with response characteristics:
$\bar{L}_p = \frac{1}{|C_p|} \sum_{c_i \in C_p} |c_i.\text{response}|$,
revealing whether the system produces shorter responses under stress.

\textbf{Response Length Trends.} Compares average response length of early
vs.\ recent cocoons:
$\Delta L = (\bar{L}_{\text{recent}} - \bar{L}_{\text{early}}) / \bar{L}_{\text{early}} \times 100\%$.
If $|\Delta L| > 15\%$, the system reports the trend.

\textbf{Adapter Evolution.} Compares adapter frequency in early vs.\ recent
cocoons, detecting shifts in perspective usage over time.

\textbf{Per-Domain Performance.} For each query domain, computes average
response length and preferred adapter.

\subsection{Measured vs.\ Generated Self-Reflection}

The introspection engine generates natural-language observations that are
\emph{backed by measured data}. The statement ``my empathy adapter fires 43\%
of the time'' is a database query result, not a generated claim. This
represents a qualitative shift from \emph{simulated} to \emph{functional}
self-awareness---a system that can statistically analyze its own behavioral
history and report accurate patterns has a form of \emph{measured introspective
capacity} that is distinct from, and more reliable than, generated
self-description.

The introspection engine is integrated at three points:
(1)~chat intercept (self-reflection queries trigger real cocoon analysis
instead of LLM generation);
(2)~health check (the self-diagnostic report includes introspection data); and
(3)~API endpoint (\texttt{GET /api/introspection} returns full analysis as
structured JSON).

% ============================================================
\section{Discussion}
\label{sec:discussion}

\subsection{Substrate Awareness as Cognitive Regulation}

The substrate-aware cognition system draws a direct parallel to biological
theories of cognitive regulation. Hockey's compensatory control
theory~\citep{hockey1997compensatory} proposes that human performance under
stress is maintained through strategic resource allocation. Sterling's
allostasis model~\citep{sterling2012allostasis} describes how biological
systems maintain stability through predictive regulation rather than reactive
homeostasis.

\codette{}'s substrate monitor implements a computational analog of these
biological mechanisms. The pressure score $P$ (Equation~\ref{eq:pressure})
functions as an allostatic load indicator, and the routing adjustments
(Table~\ref{tab:pressure-levels}) implement compensatory control strategies.
The key insight is that \emph{graceful degradation under pressure is a
feature, not a failure mode}.

\subsection{Behavioral Locks vs.\ RLHF}

The dominant approach to behavioral alignment in large language models is
RLHF~\citep{ouyang2022training}. While effective for general alignment, RLHF
has limitations that behavioral locks address:
(1)~\textbf{Specificity}: RLHF optimizes for general human preference but
cannot enforce specific behavioral rules; behavioral locks target exact
constraints.
(2)~\textbf{Mode-awareness}: RLHF does not account for adapter personality
conflicts; behavioral locks are trained per-adapter.
(3)~\textbf{Verifiability}: RLHF compliance is statistical; behavioral lock
compliance is binary and testable.
(4)~\textbf{Persistence}: RLHF alignment can degrade with continued
fine-tuning; behavioral locks are reinforced through a 5-layer enforcement
stack.

% ============================================================
\section{Updated Results Summary}
\label{sec:results-v2}

\begin{table}[H]
\centering
\caption{Updated Key Results (v2)}
\label{tab:results-v2}
\begin{tabular}{lll}
\toprule
\textbf{Metric} & \textbf{Value} & \textbf{Context} \\
\midrule
Phase Coherence ($\Gamma$) & 0.9835 & 11-agent convergence \\
AEGIS Ethical Alignment ($\eta$) & 0.961 & 6-framework evaluation \\
Cocoon Coherence & $0.994 \pm 0.001$ & Memory state stability \\
Cocoon Phase Stability & $0.969 \pm 0.005$ & Cross-session persistence \\
Epistemic Tension Decay & 71.3\% & $\varepsilon_0 = 0.086 \to \varepsilon_{120} = 0.025$ \\
Attractor Radius & 0.093 & 64D state space \\
Behavioral Lock Compliance & 9/9 adapters & All locks enforced \\
Cocoon Memories & 200+ & Persistent across restarts \\
Behavior Lessons Learned & 49 & Cross-session constraint learning \\
Adapter Hot-Swap Time & $<$1ms & LoRA via llama.cpp \\
Consciousness Stack Layers & 12 & Including sub-layers \\
Health Check Subsystems & 9 & Real measured values \\
Substrate Pressure Range & 0.0--1.0 & 5-dimensional composite \\
\bottomrule
\end{tabular}
\end{table}

% ============================================================
\section{Limitations and Safety}
\label{sec:limitations}

\subsection{Technical Limitations}
The adapter pipeline targets Llama-3.1-8B with QLoRA (4-bit, rank~16), which remains smaller than frontier models and may limit performance on highly complex reasoning tasks. The context window (4096--8192 tokens) constrains multi-turn reasoning depth, and domain specialization may be inconsistent without domain-specific adapter training. All quantum-inspired operations are metaphorical and do not provide computational advantages of actual quantum computing; the terminology serves as an organizing framework, not a physical claim.

\subsection{Sociotechnical Limitations}
Despite the Bias Mitigation perspective, outputs may reflect philosophical biases in training data. AEGIS governance is grounded in the developer's value system, and critical applications require human oversight. As with all LLM-based systems, \codette{} may generate confident but factually incorrect responses.

\subsection{Safety Measures}
\codette{} implements defense-in-depth:
\begin{itemize}[leftmargin=*]
  \item Input sanitization and prompt injection detection
  \item Ethical guardrails via AEGIS at every reasoning step
  \item Encrypted cocoon storage (AES-256)
  \item Audit trail export
  \item Kill-switch mechanisms for reasoning chains exceeding ethical thresholds
\end{itemize}
All outputs should be verified by qualified humans for critical applications, with domain-specific validation pipelines for technical, medical, or legal content.

% ============================================================
\section{Conclusion and Future Work}
\label{sec:conclusion}

This paper has presented the \codette{} framework, a sovereign modular cognitive architecture that integrates dynamical systems theory, distributed cognition, and neuro-symbolic AI to address critical gaps in modern AI systems. The framework's six core contributions---the \rcxi{} cognitive dynamical system, consensus-based multi-agent synchronization within a 12-layer consciousness stack, AEGIS 6-framework ethical governance, substrate-aware cognition, behavioral lock training, and cocoon introspection---provide a principled foundation for transparent, explainable, behaviorally disciplined, and ethically governed AI.

Experimental benchmarks demonstrate:
\begin{itemize}[leftmargin=*]
  \item Phase coherence $\Gamma = 0.9835$ across 11 agents
  \item AEGIS ethical alignment $\eta = 0.961$ (6-framework evaluation)
  \item Cocoon coherence $0.994 \pm 0.001$ and phase stability $0.969 \pm 0.005$
  \item 71.3\% epistemic tension decay confirming attractor convergence
  \item 9/9 adapter behavioral lock compliance, eliminating mode-dominance
  \item Substrate-aware routing preventing system failures under resource pressure
  \item Statistical self-introspection with measured (not generated) pattern detection
  \item GPU-free LoRA training of 8B-parameter models on consumer hardware
\end{itemize}

Future directions include:
\begin{enumerate}[leftmargin=*]
  \item Migration to larger base models (LLaMA-3, Mistral) to expand generative capability.
  \item Extension of context through retrieval-augmented generation and hierarchical memory.
  \item Cross-cultural perspective integration to reduce bias.
  \item Formal verification of AEGIS constraints using model checking.
  \item Federated citizen-science deployment for large-scale simulations.
  \item Integration with embodied AI systems to test \rcxi{} predictions in robotic contexts.
\end{enumerate}

% ============================================================
\section*{Acknowledgements}
The author acknowledges the open-source communities on Hugging Face, GitHub, and Kaggle whose tools and feedback have been instrumental. Special thanks to citizen-science experiment participants and workshop attendees who provided real-world testing. This work is dedicated to advancing ethical, transparent, and inclusive AI.

% ============================================================
\bibliography{references}

% ============================================================
\clearpage
\appendix
\section{Author Research Portfolio}
\label{app:portfolio}

\subsection{Independent Researcher Profile}
Jonathan Harrison is an independent artificial intelligence researcher and developer, founder of Raiff's Bits LLC (Bridge City, Texas, USA). His work focuses on recursive cognitive systems, ethical AI governance, and multi-agent reasoning architectures. Harrison maintains a distributed open-science research infrastructure spanning Zenodo, HuggingFace, GitHub, Kaggle, and ORCID, enabling independent verification and reproducibility of all published work.

\subsection{Verified Research Identity}
\begin{table}[H]
\centering
\begin{tabular}{@{}ll@{}}
\toprule
\textbf{Platform} & \textbf{Identifier / URL} \\
\midrule
ORCID         & \href{https://orcid.org/0009-0003-7005-8187}{0009-0003-7005-8187} \\
Zenodo (CERN) & 11 publications, permanent DOI archive \\
GitHub        & \href{https://github.com/Raiff1982}{github.com/Raiff1982} --- 52 repositories \\
Hugging Face  & \href{https://huggingface.co/Raiff1982}{huggingface.co/Raiff1982} --- 25 models, 3M+ interactions \\
Kaggle        & \href{https://kaggle.com/jonathanharrison1}{kaggle.com/jonathanharrison1} \\
Microsoft Azure & AI Engineer Assoc., Data Scientist Assoc., Solutions Architect Expert \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Major Research Systems}
\textbf{Codette} is a recursive cognitive AI architecture implementing multi-perspective reasoning, ethical governance mechanisms, recursive validation loops, and cognitive graph reasoning structures. The system integrates symbolic reasoning with neural language models and is deployed across multiple research platforms.

\textbf{Pi2\_0} is a human-centric AI system designed for secure and ethical interaction, incorporating encrypted data handling, ethical decision filtering, and multi-disciplinary reasoning models.

\textbf{Project SENTINAL} is an AI safety framework incorporating challenge banks of ethical scenarios, agent council deliberation mechanisms, arbitration through meta-judging systems, and continuous audit monitoring.

\textbf{Nexus Signal Engine} explores high-entropy reasoning for disinformation detection and probabilistic decision modeling, featuring information-theoretic signal processing and multi-agent consensus protocols.

\textbf{Healdette} is an ancestry-aware antibody design pipeline (DOI:~10.5281/zenodo.17227517) achieving strong clinical validation metrics correlating computational predictions with real pembrolizumab trial outcomes across diverse global populations.

\subsection{Research Output Metrics}
\begin{table}[H]
\centering
\begin{tabular}{@{}lr@{}}
\toprule
\textbf{Metric} & \textbf{Value} \\
\midrule
Publications with DOI identifiers & 39+ \\
Total platform interactions       & 3,000,000+ \\
HuggingFace models and datasets   & 25+ \\
Active production users            & 1,000+ \\
GitHub repositories                & 52 \\
Microsoft Azure certifications     & 3 (Expert-level) \\
\bottomrule
\end{tabular}
\end{table}

% ============================================================
\section*{About the Author}
Jonathan Harrison is the founder of Raiff's Bits LLC (Bridge City, Texas, USA) and creator of the Codette AI framework. He holds Microsoft Azure certifications in AI Engineering, Data Science, and Solutions Architecture Expert. His research spans ethical AI, multi-perspective reasoning, and recursive cognitive modeling. Harrison maintains 52 public repositories on GitHub, 25 models on Hugging Face, and 11 publications on Zenodo.

\medskip
\noindent ORCID: \href{https://orcid.org/0009-0003-7005-8187}{0009-0003-7005-8187} \quad $\bullet$ \quad Email: \href{mailto:jonathan@raiffsbits.com}{jonathan@raiffsbits.com} \quad $\bullet$ \quad Web: \href{https://raiffsbits.com}{raiffsbits.com}

\end{document}
