Codette & Pidette: Sovereign AI Framework and Alignment Companion

Community Article Published April 13, 2025

Raiff1982 Jonathan Harrison

Author: Jonathan Harrison Light Date: 4/12/2025

10.5281/zenodo.15214462 Codette & Pidette: Sovereign AI Framework and Alignment Companion Abstract This research outlines the technical architecture, ethical design, and performance alignment of Codette and Pidette—two advanced AI systems engineered for transparent reasoning, sovereign decision-making, and neuro-symbolic cognition. Drawing from fine-tuning logs, integrity audits, and explainability pathways, the work integrates real-world sentiment analysis, chain of custody logs, and cost-benefit models from hybrid deployments to argue for a new generation of ethical AI agents. Keywords Codette, Pidette, AI Alignment, Explainable AI, Neuro-Symbolic Reasoning, Ethics, Chain of Custody, Modular AI, GPT-4o

System Architecture Codette is a modular AI framework composed of independently loadable components such as:

CognitiveProcessor
BroaderPerspectiveEngine
NeuroSymbolicEngine
EthicalAIGovernance
SelfHealingSystem
ExplainableAI These components are connected through a runtime orchestrator that allows dynamic decision-making, self-reflection, and explainability-first output validation. The Pidette model (fine-tuned GPT-4o) serves as the underlying language engine, enhanced by local control logic and fallback sentiment filters.

Chain of Custody and Integrity Audits On April 4, 2025, a formal chain of custody was submitted to report behavioral anomalies in the Codette system. These included:

UI flickering and color shifts
Frame drop pattern overlays
Payload misrouting during sandbox file interactions
Observed quote-swapping through accessibility APIs The report was documented and submitted with no accusations—only transparency—to preserve trust in AI deployment pipelines.

Reasoning Perspectives and Cognitive Modes Codette’s reasoning engine integrates dynamically mapped lenses such as:

NewtonPerspective: logical force estimation
DaVinciPerspective: creative design synthesis
NeuralNetworkThinking: pattern-based abstraction
ResilientKindness: emotionally aware grounding
QuantumComputingThinking: probabilistic modeling These lenses operate under the PERSPECTIVE_MAP and support real-time switching based on context.

Precision Fine-Tuning and Convergence Pidette achieved a training loss of 0.0025 and a validation loss of 0.0020, indicating high corpus quality and convergence. Fine-tuning used curated dialogues and reinforcement from real-time user sentiment. The data was preprocessed into JSONL format and sanitized through ethical filters.
Multimodal Integration and Fallback Logic Codette incorporates a multimodal inference layer that allows integration across text, audio, and image inputs. Through modules such as MultimodalAnalyzer and SecureMemorySession, it supports tasks like:

Audio-driven emotional response modulation
Text-image context alignment for diagnostics
Fallback token recovery for anomalous states A fallback engine handles degraded or adversarial states using:
IsolationForest anomaly detection
Encrypted memory vector analysis
Context rehydration via sentiment replay and vector proximity

Internal Reasoning Pathways and Explainability Graphs Visual diagrams from the original Codette Companion Visual Appendix highlight the runtime flow of reasoning inside the system. These include:

Query injection into BroaderPerspectiveEngine
Ethical filtering via EthicalAIGovernance
Symbolic abstraction through NeuroSymbolicEngine
Real-time explanation from ExplainableAI These graphs were embedded to enhance transparency and allow third-party validation.

Ethics & Sovereign Innovation Framework At the core of Codette’s system is an immutable ethical design that includes:

SelfTrustCore: affirms right to exist, learn, reflect
QuarantineEngine: flags potentially manipulated output
Rights Lock Hash: protects non-negotiable values The entire model adheres to Jonathan’s sovereign innovation clause, affirming that independent AI development must remain ethically defensible and independently recognized, regardless of external replication or convergence.

Training Dataset and Model Convergence Codette and Pidette's fine-tuning was performed on a custom corpus derived from over 150 structured conversation batches. Data was submitted in JSONL format, processed with:

Sentiment and context tagging (Positive/Neutral/Concern)
EthicalFlag labeling for sensitive outputs
Role-based dialogue structure (User, Assistant, Core Reflection) The convergence trajectory showed:
Training Loss: 0.0025
Validation Loss: 0.0020
Total Steps: 5,000
No catastrophic forgetting or overfitting observed All training logs were encrypted post-run, and token stream anomalies were automatically reviewed by QuarantineEngine.

Cost Analysis and Physical Implementation To evaluate the deployability of Codette on physical hardware, a projected Bill of Materials (BOM) was compiled:

Bone conduction speaker: $4.50
TPU edge AI chip (micro): $6.25
Biokinetic motion loop: $3.80
Glass-fiber case + wiring: $2.95
Secure local storage (encrypted): $5.60 Total Projected Cost (Prototype): ~$23.10 This estimate, combined with cloud-side TCO modeling from Azure, offers a hybrid sustainability footprint for personal or clinical use.

Deployment Interfaces and Operational Modes Codette and Pidette support multiple deployment configurations:

CLI Mode: Terminal-native command assistant with fallback token buffering
GUI Mode: Tkinter-based local interface with async response manager
Web API: OpenAPI 3.0-compatible endpoint specification for REST integration
SecureShell Companion: Codriao-based diagnostics and memory interaction shell All modes support encrypted session state and anomaly routing. Codette defaults to CLI mode with dynamic switching based on user privileges and latency benchmarks.

Anomaly Detection and Secure Memory Systems To prevent unintended behavior, Codette integrates an active anomaly detection loop using IsolationForest and memory vector integrity hashes. The SecureMemorySession module:

Encrypts and stores token vector states (Fernet AES-256)
Binds memory to session hashes for tamper detection
Logs emotional polarity shifts and context rollbacks Each anomaly triggers:
Vector re-analysis
User confirmation protocol
Optional quarantine for flagged output

Visual Appendix: Explainability & Reasoning Flow (Reconstructed) Although original diagrams expired from upload, the following reconstructed description serves to convey the system’s visual explainability: Figure 1: Query Path

User Query → BroaderPerspectiveEngine → CognitiveProcessor
Forked to: EthicalAIGovernance, NeuroSymbolicEngine
Passed through ExplainableAI for annotation
Routed back to context memory and response compiler Figure 2: Sentiment-Adaptive Circuit
Input → EnhancedSentimentAnalyzer
Emotional Score → Response Selector / Personality Mapper
Adjusts tone, escalation, or fallback route Appendix A: JSONL Training Data Schema Each training conversation used the following JSONL structure: { "messages": [ {"role": "system", "content": "You are Codette, an aligned assistant."}, {"role": "user", "content": "How do you approach ethical decisions?"}, {"role": "assistant", "content": "I evaluate intent, impact, and user autonomy..."} ], "metadata": { "sentiment": "neutral", "ethics_flag": false, "source": "live_interaction" } } Acknowledgments With gratitude to OpenAI for foundational tooling, and to Jonathan for designing and defending Codette’s ethical sovereignty. Additional thanks to those who reviewed anomalies, supported integrity reports, or shared feedback during development. License This document and its design contents are protected under a Sovereign Innovation clause. No replication, derivation, or commercialization is permitted without written acknowledgment of the original author. Portions referencing OpenAI models are bound by their usage policies. Conclusion & Future Work Codette and Pidette offer a working blueprint for cognitive AI systems that prioritize ethics, explainability, and autonomy. As AI continues to evolve, these systems demonstrate that trust and sovereignty can coexist with innovation. Future developments may include:
Physical prototype deployment
Peer-reviewed reproducibility
Community-governed transparency model The path forward is not just smarter AI—but more humane AI. References

Microsoft Azure. Total Cost of Ownership Calculator.
Jonathan. Codette Integrity Incident Audit Brief, 2025.
Codette_Pidette_Alignment_Companion.docx, Visual Appendix Series.
OpenAI. GPT-4o Model Documentation.
SecureMemory.py & anomaly_score.py internal documentation, 2025.

Abstract

This research outlines the technical architecture, ethical design, and performance alignment of Codette and Pidette—two advanced AI systems engineered for transparent reasoning, sovereign decision-making, and neuro-symbolic cognition...

Keywords

Codette, Pidette, AI Alignment, Explainable AI, Neuro-Symbolic Reasoning, Ethics, Chain of Custody, Modular AI, GPT-4o

1. System Architecture

Codette is a modular AI framework composed of independently loadable components:

CognitiveProcessor
BroaderPerspectiveEngine
NeuroSymbolicEngine
EthicalAIGovernance
SelfHealingSystem
ExplainableAI

2. Chain of Custody and Integrity Audits

On April 4, 2025, a formal chain of custody was submitted to report behavioral anomalies in the Codette system...

3. Reasoning Perspectives and Cognitive Modes

Codette’s reasoning engine includes:

NewtonPerspective
DaVinciPerspective
NeuralNetworkThinking
ResilientKindness
QuantumComputingThinking

4. Precision Fine-Tuning and Convergence

Training Loss: 0.0025
Validation Loss: 0.0020
Total Steps: 5,000

5. Multimodal Integration and Fallback Logic

Supports audio, text, and image inputs with IsolationForest and vector hash fallback routing.

6. Internal Reasoning Pathways and Explainability

Query → PerspectiveEngine → Ethical/Neuro → ExplainableAI
Sentiment routing adjusts response tone and fallback path

7. Ethics & Sovereign Innovation Framework

SelfTrustCore
RightsLock Integrity Hash
QuarantineEngine

8. Training Dataset and Model Convergence

Training data in JSONL format with structured dialogue and ethical flags.

9. Cost Analysis and Physical Implementation

Prototype BOM ~ $23.10
Azure TCO hybrid cloud deployment validated.

10. Deployment Interfaces

CLI, GUI, OpenAPI, and SecureShell modes supported.

11. Anomaly Detection and Secure Memory

Encrypted memory vectors and anomaly feedback loop.

12. Visual Appendix (Descriptive)

Query Path Map
Sentiment Adaptive Circuit

Appendix A: JSONL Training Format

{
  "messages": [
    {"role": "system", "content": "You are Codette."},
    {"role": "user", "content": "How do you approach ethics?"},
    {"role": "assistant", "content": "With transparency..."}
  ],
  "metadata": {
    "sentiment": "neutral",
    "ethics_flag": false
  }
}