Recursive Containment Framework

A recursive alignment framework for cognitive coherence, belief tracking, and contradiction repair.

Table of Contents

• Introduction
• Features
• Topics
• Installation
• Usage
• Contributing
• License
• Contact
• Releases

Introduction

The Recursive Containment Framework (RCF) aims to enhance cognitive systems by providing tools for alignment, belief tracking, and contradiction repair. This framework is designed for researchers and developers interested in AI safety and cognitive systems. By utilizing recursive modeling techniques, RCF allows for self-correction and effective identity modeling.

Features

• Cognitive Coherence: Ensures that beliefs within a system remain consistent and aligned.
• Belief Tracking: Monitors and updates beliefs over time to reflect new information.
• Contradiction Repair: Identifies and resolves inconsistencies in beliefs.
• Recursive Modeling: Uses recursive techniques to build complex models that adapt over time.
• Internal Alignment: Focuses on aligning internal processes with external goals.
• Pseudonymous Research: Encourages collaborative research while maintaining user privacy.
• Systems Architecture: Provides a structured approach to building cognitive systems.

Topics

This framework covers a range of important topics, including:

• AI Safety
• Alignment
• Belief Tracking
• Cognitive Systems
• Containment
• Identity Modeling
• Internal Alignment
• Pseudonymous Research
• Recursive Modeling
• Self-Correction
• Systems Architecture

Installation

To install the Recursive Containment Framework, follow these steps:

1. Clone the repository:

   git clone https://github.com/Jonathanmutu/recursive-containment-framework.git

2. Navigate to the project directory:

   cd recursive-containment-framework

3. Install the required dependencies:

   pip install -r requirements.txt

4. Ensure that you have the necessary environment variables set up.

Usage

To use the Recursive Containment Framework, follow these steps:

1. Import the necessary modules in your Python script:

   from rcf import BeliefTracker, ContradictionRepair

2. Create an instance of the BeliefTracker:

   tracker = BeliefTracker()

3. Add beliefs to the tracker:

   tracker.add_belief("The sky is blue.")

4. Check for contradictions:

   contradictions = tracker.check_contradictions()

5. Repair contradictions if found:

   if contradictions:
       repair = ContradictionRepair()
       repair.fix(contradictions)

For detailed examples and advanced usage, please refer to the documentation.

Contributing

We welcome contributions to the Recursive Containment Framework. If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your branch to your forked repository.
5. Create a pull request.

Please ensure that your code adheres to our coding standards and includes tests.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For questions or feedback, please reach out to the maintainer:

• Name: Jonathan Mutu
• Email: jonathanmutu@example.com

Releases

To download the latest version of the Recursive Containment Framework, visit our Releases section. You can find compiled binaries and other useful files there.

You can also check the Releases section for updates and new features.

Conclusion

The Recursive Containment Framework provides a robust platform for exploring cognitive systems and AI safety. By focusing on alignment and contradiction repair, this framework helps create more reliable and coherent systems. We invite you to explore, contribute, and improve the framework as we work towards a safer AI future.