TGCSM-CIRCUIT 🧠🔗

Welcome to the TGCSM-CIRCUIT repository! This project serves as the original containment framework for recursion-stable cognition, collapse-resistant logic, and LLM self-reflection. We aim to provide a robust foundation for developing advanced AI systems that prioritize safety and reliability.

Table of Contents

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

Introduction

The TGCSM-CIRCUIT framework is designed to address some of the most pressing challenges in AI development today. With a focus on recursion stability and collapse resistance, this framework provides tools and methodologies for building AI systems that can reflect on their own logic and reasoning processes. Our goal is to enhance AI safety while ensuring that these systems remain efficient and effective.

Key Features

• Recursion-Stable Cognition: This feature allows AI systems to maintain stability in their reasoning processes, even when faced with complex recursive tasks.

• Collapse-Resistant Logic: Our framework ensures that AI systems can operate without falling into logical traps or inconsistencies.

• LLM Self-Reflection: The framework supports large language models (LLMs) in evaluating their own outputs, fostering self-improvement and reliability.

• Rail Detection: A specialized tool for identifying potential logical fallacies and ensuring the integrity of AI reasoning.

Topics

This repository covers a wide range of topics relevant to modern AI research and development:

• AI
• AI Safety
• Circuit Framework
• Collapse-Resistant AI
• Gödel's Incompleteness Theorems
• Halting Problem
• Large Language Models (LLM)
• LLM Evaluation
• Rail Detection
• Recursive Containment

Installation

To get started with TGCSM-CIRCUIT, you need to clone the repository and install the necessary dependencies. Here’s how to do it:

1. Clone the repository:

   git clone https://github.com/Atorpor/TGCSM-CIRCUIT.git

2. Navigate to the project directory:

   cd TGCSM-CIRCUIT

3. Install the required packages. If you are using Python, you can use pip:

   pip install -r requirements.txt

4. If you need to download and execute the latest release, please visit the Releases section.

Usage

Once you have installed the framework, you can start using it for your AI projects. Here’s a simple example of how to initialize the framework:

from tgcs_circuit import TGCSMCircuit

# Initialize the circuit
circuit = TGCSMCircuit()

# Run a sample process
circuit.run_sample_process()

For more detailed usage instructions, refer to the documentation included in the docs folder.

Contributing

We welcome contributions from the community. If you would like to contribute to TGCSM-CIRCUIT, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeature

3. Make your changes and commit them:

   git commit -m "Add your feature description"

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a pull request.

Please ensure that your code follows our coding standards and that you include tests for any new features.

License

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

Contact

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

• Your Name: your.email@example.com

Releases

To download the latest version of TGCSM-CIRCUIT, please visit the Releases section. You can find executable files and additional release notes there.

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We hope you find TGCSM-CIRCUIT useful for your AI projects. Your feedback and contributions are vital to the success of this framework. Thank you for being part of our community!