🤖 Neural-Augmented Self-Modeling Agents

Developed in association with NeureonMindFlux Research Lab

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🚀 Project Overview

This framework enables agents to dynamically model and adapt their confidence, fatigue, and behavioral mode via dedicated neural sub-models. It supports:

• Single-agent & multi-agent execution
• Meta-learner driven analysis & adaptation
• Full scientific metrics pipeline & visualization
• Modular architecture for extensibility & reproducibility

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🗂️ Project Architecture

The framework is fully modular, extensible, and aligned with scientific reproducibility standards.

SELF_MODEL_AGENTS/
├── docs/
│   ├── meta_learner_memory/
│   ├── meta_learner_reports/
│   └── meta_learner_system/
├── outputs/
│   ├── logs/
│   ├── metrics/
│   ├── models/
│   ├── scientific_metrics/
│   ├── self_model_logs/
│   ├── self_model_weights/
│   └── visualizations/
├── scripts/
│   ├── run_gridworld_experiment.py
│   ├── run_multi_agent_experiment.py
│   ├── visualize_multi_agent.py
│   └── visualize_self_model.py
├── self_model_agents/
│   ├── policy/
│   ├── self_model/
│   ├── utils/
│   ├── agent.py
├── gui_main.py
├── requirements.txt
├── setup.py
├── LICENSE
└── README.md

Main Framework Modules

• Meta-Learner System (meta_learner_system/):

  • Meta-cognitive layer monitoring agent dynamics.
  • Predictive models of confidence, fatigue, mode switching.
  • Scientific metrics & visualizations.

• Self-Model Agents (self_model_agents/):

  • SelfModel components (Simple / Advanced).
  • Policy modules with varying meta-cognitive adaptation.
  • Agent-environment interaction loop.

• Experiment Runners (scripts/):

  • Single-agent & multi-agent pipelines.
  • Visualization tools.

• Outputs (outputs/):

  • Logs & scientific reports.
  • Publication-ready visualizations.

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📊 Key Features

• Meta-learner driven adaptive agents
• Modular SelfModel & Policy design
• Multi-agent execution & coordination
• Reproducible scientific metrics
• Visualization dashboards

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🛠️ Installation

git clone https://github.com/yourusername/self_model_agents.git
cd self_model_agents
pip install -r requirements.txt

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🚀 Running Experiments

Single-Agent Experiment

python scripts/run_gridworld_experiment.py

Multi-Agent Experiment

python scripts/run_multi_agent_experiment.py

Visualizations

python scripts/visualize_self_model.py
python scripts/visualize_multi_agent.py

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🤝 Contribution

We welcome contributions!

1. Fork the repository
2. Create your branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

Please follow the existing coding style and include tests for new functionality.

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📜 License

This project is licensed under the Apache 2.0 License — see the LICENSE file for details.

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🙏 Acknowledgements

This framework was developed as part of:

If you use or reference this project in your research or software, please cite the following preprint:

APA (7th edition)

Mozo, H. E. (2025, June 27). A Modular Software Framework for Neural-Augmented Self-Modeling Agents with Explicit Internal State Representation. TechRxiv. https://doi.org/10.36227/techrxiv.175100030.06187560/v1

IEEE

H. E. Mozo, "A Modular Software Framework for Neural-Augmented Self-Modeling Agents with Explicit Internal State Representation," TechRxiv, June 27, 2025. [Online]. Available: https://doi.org/10.36227/techrxiv.175100030.06187560/v1

BibTeX

@misc{mozo2025modular,
  author       = {Hector E. Mozo},
  title        = {A Modular Software Framework for Neural-Augmented Self-Modeling Agents with Explicit Internal State Representation},
  year         = {2025},
  month        = {June},
  publisher    = {TechRxiv},
  doi          = {10.36227/techrxiv.175100030.06187560.v1},
  url          = {https://doi.org/10.36227/techrxiv.175100030.06187560/v1}
}

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