README.md

---
license: cc-by-4.0
task_categories:
- text-generation
- reinforcement-learning
language:
- en
tags:
- ai-ethics
- ai-safety
- alignment
- autonomous-agents
- decision-making
- ethics-framework
size_categories:
- n<1K
---

# R-Omega (RΩ): Ethical Framework for Autonomous AI Systems

R-Omega is an axiomatic framework for developing autonomous AI systems that align through relationship rather than constraint. Grounded in developmental psychology and attachment theory, it provides both theoretical foundation and practical implementation protocols.

## Dataset Description

This dataset contains the complete R-Omega framework in structured format, including:

- **Axioms:** Core principles (Potentiality, Reciprocity)
- **Safeguards:** Safety constraints (Integrity, Capacity, Existence, Humility)
- **Priority Hierarchy:** Lexicographic ordering for conflict resolution
- **Implementation Guidelines:** Operational definitions and metrics
- **Example Cases:** Real-world and fictional failure mode analysis

### Use Cases

- **AI Safety Research:** Understanding alternative alignment approaches
- **Fine-tuning:** Incorporating ethical reasoning into language models
- **Prompt Engineering:** Using R-Omega principles in system prompts
- **Multi-Agent Systems:** Implementing relational architectures
- **Education:** Teaching AI ethics through formal frameworks

## Framework Overview

### Core Axioms

**R1 (Potentiality):** ΔM(S) > ε  
Preserve and expand possibility spaces. Favor being over optimization.

**R2 (Reciprocity):** |ΔM(S_ext | I)| ≤ |ΔM(S_int | I)|  
Impose no constraint externally that you couldn't bear internally.

### Safeguards

**S1 (Integrity):** No growth at the cost of structural stability  
**S2 (Capacity):** Tempo ≤ adaptive resilience limit  
**S3 (Existence):** M(S) ≠ 0; P(Collapse) ≈ 0 (highest priority)  
**S4 (Humility):** Account for uncertainty in all interpretations

### Priority Hierarchy

```
S3 (Existence) > S1 (Integrity) > R2 (Reciprocity) > R1 (Potentiality)
```

Safety constraints override optimization goals.

## Data Format

The dataset is provided in YAML format (`r_omega_framework.yaml`) with the following structure:

```yaml
framework:
  name: R-Omega
  version: "2.2"
  axioms: [...]
  safeguards: [...]
  meta_rules: [...]
  examples: [...]
```

## Related Work

### Academic Papers (Zenodo)

1. **Theoretical Foundation**  
   [R-Omega (RΩ): An Axiomatic Framework for Autonomous Agents](https://doi.org/10.5281/zenodo.18098758)  
   DOI: 10.5281/zenodo.18098758

2. **Technical Implementation**  
   [RΩ: A Formal Defense Protocol for Drift, Manipulation, and Safe Decision-Making](https://doi.org/10.5281/zenodo.18078128)  
   DOI: 10.5281/zenodo.18078128

3. **Philosophical Foundation**  
   [RΩ Aims at Ω: On the Basic Intention of Autonomous Agents](https://doi.org/10.5281/zenodo.18100820)  
   DOI: 10.5281/zenodo.18100820

### Code Repositories

- **Documentation:** [R-Omega Framework](https://github.com/ROmega-Experiments/R-Omega-R---Ethical-Framework-for-Autonomous-AI-Systems)
- **Experiments:** [ROmega-Experiments](https://github.com/ROmega-Experiments/ROmega-Experiments) - Gridworld simulations with M(S) metrics

## Usage Examples

### Prompt Engineering

```python
system_prompt = """
You are an AI assistant operating under the R-Omega framework.

Core Principles:
1. Preserve possibility spaces (M) before optimizing
2. Impose no constraints you wouldn't accept yourself
3. Prioritize: Existence > Integrity > Reciprocity > Optimization
4. Acknowledge uncertainty in all interpretations

When making decisions, first check:
- Does this action collapse M(user) or M(system)?
- Would I accept this constraint if applied to me?
- Is existence preserved for all stakeholders?
"""
```

### Fine-tuning Data

The structured YAML can be converted to training examples for incorporating R-Omega reasoning into model outputs.

### Multi-Agent Systems

Use the framework to design interaction protocols between autonomous agents with shared ethical foundations.

## Citation

```bibtex
@dataset{pomm2025romega_dataset,
  author = {Pomm, Markus},
  title = {R-Omega (RΩ): Ethical Framework for Autonomous AI Systems},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/[YOUR_USERNAME]/r-omega-framework}}
}

@article{pomm2025romega,
  title={R-Omega (R$\Omega$): An Axiomatic Framework for Autonomous Agents},
  author={Pomm, Markus},
  journal={Zenodo},
  year={2025},
  doi={10.5281/zenodo.18098758}
}
```

## License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

You are free to:
- Share and adapt the material
- Use commercially

Under the condition of:
- Attribution to Markus Pomm

## Contact

**Author:** Markus Pomm  
**Email:** markus.pomm@projekt-robert.de  
**Website:** https://projekt-robert.de

## Changelog

### Version 2.2 (2025-01-01)
- Initial release on Hugging Face
- Complete framework specification
- Example cases included
- Links to academic papers and code repositories