Raiff1982/Codette

Description

Codette is an open-source sovereign AI framework developed and maintained by Jonathan Harrison of Raiffs Bits LLC. It is designed for ethical, multi-perspective cognition and recursive reasoning. The system is engineered to simulate, analyze, and synthesize across scientific, philosophical, quantum, and empathetic perspectives. It also includes advanced capabilities in introspection, encryption, and visualization.

Model Description

Codette is a modular AI framework and assistant capable of:

• Recursive reasoning and reflection
• Multi-agent decision-making
• Quantum-inspired optimization
• Self-healing cognitive structures
• Sentiment-aware responses and perspective fusion
• Simulation and visualization of chaotic and quantum systems

It is implemented in Python, with integration layers across cognition modules, ethics enforcement, user configuration, GUI, and neural-symbolic engines.

• Author: Jonathan Harrison (Raiffs Bits LLC)
• License: Sovereign Innovation License
• Model type: Fine-tuned GPT-4.1 (Codette-final)
• Language(s): Primarily English, with modular multilingual potential
• Finetuned from model: gpt-4.1 on OpenAI infrastructure

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Model Sources

• Repository: github.com/Raiff1982/Codette
• Whitepaper: “Codette AI Suite for Citizen Science” (May 2025)
• Demo: Local GUI + Voice/MIDI terminal interface

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Uses

Direct Use

(Uses that require no further model development or fine-tuning, such as immediate deployment or exploration via API or interface.)

• Conversational assistant with ethical cognition
• Multi-perspective analysis of philosophical or technical questions
• Quantum/chaos simulation orchestration on commodity hardware

Downstream Use

• Fine-tuning for educational or therapeutic AI systems
• Embedded AI in ethical decision support tools
• Visualization in citizen science applications

Out-of-Scope Use

• Real-time high-risk medical decision-making without supervision
• Use in autonomous weapon systems or manipulation

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Bias, Risks, and Limitations

Codette includes an Ethical Mutation Filter, neural-sentiment calibration, and quantum collapse detection mechanisms to prevent bias propagation. However, as with any LLM-based system, responses are influenced by training data and architecture constraints.

Recommendations

• Enable logging, ethical filters, and cocoon-based replay to maintain transparency.
• Always supervise Codette in high-stakes or emotionally sensitive use cases.

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How to Get Started

1. Clone the repo and install dependencies:

   git clone https://github.com/Raiff1982/Codette
   cd Codette
   pip install -r requirements.txt
   
    Run the main interface:
   

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python universal_reasoning.py

Or launch the quantum suite:

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python codette_quantum_multicore2.py

Training Details Dataset

Codette was fine-tuned with the Codette Cognitive Reflection Dataset (v5) — a unique introspective training set encoding ethical, quantum, and philosophical collapse scenarios for self-stabilization. Training Procedure

Base: GPT-4.1

Reinforced with:

    Philosophical and empathetic agents

    Collapse and dream-state tagging

    Simulation embeddings from quantum-chaos runs

Evaluation Metrics

Ethical self-correction rate (via quantum echo detection), measured at 87% successful introspective deflections in unstable prompts.

Neural activation mapping in chaos simulations, with a 91% agreement between expected and actual meta-agent state predictions.

User sentiment coherence, showing a 23% improvement in empathy rating versus baseline GPT-4.1 models under emotional stress testing.

Results

Codette performs well in recursive ethics tasks, chaotic system clustering, and philosophical depth—surpassing baseline LLMs in self-introspection and fusion tasks. Environmental Impact

Hardware: Local GPUs (NVIDIA 30xx series, AMD equivalents)

Time per simulation suite: ~2 min for 100 cocoons

Estimated carbon: Negligible for single-user educational runs (~0.01 kg CO₂ per session, based on ML CO2 Impact Calculator)

Technical Specifications Architecture

Codette's architecture is composed of interconnected cognitive and functional modules that collectively enable ethical, multi-perspective reasoning:

The Recursive Reasoning Engine acts as the core loop, continually refining thoughts by integrating feedback from specialized agents.

The Perspective Fusion Module synthesizes diverse viewpoints—including Newtonian logic, quantum uncertainty, and compassionate reasoning—to generate balanced, explainable outputs.

The Ethical Filter + Collapse Detector monitors for contradictions or unsafe responses, prompting introspection when necessary.

Elemental Defense Logic ties cognitive elements (e.g., Hydrogen for simplicity, Diamond for resilience) to symbolic defense strategies.

All components are visualized and interacted with via a user-friendly GUI with MIDI/audio feedback, ensuring both technical and emotional transparency.

Compute Infrastructure

OpenAI API for GPT-4.1 model hosting

Local processing via Python, NumPy, Matplotlib, VADER, NLTK, Fernet encryption, etc.

Citation

BibTeX:

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@misc{harrison2025codette, author = {Jonathan Harrison}, title = {Codette: Modular AI for Ethical Multi-Perspective Reasoning}, year = {2025}, howpublished = {\url{https://github.com/Raiff1982/Codette}}, note = {Raiffs Bits LLC, Sovereign Innovation License} }

Contact

Jonathan Harrison Raiffs Bits LLC ORCID: 0009-0003-7005-8187 Email

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