O-MoE: Optimized Mixture of Experts for Defensive and Specialized AI Systems

Author: Mouad Tarif
Date: 8 August 2026
Type: Theoretical Framework

Summary

O-MoE is a theoretical framework designed to enhance Mixture of Experts (MoE) architectures by integrating:

• Sequential defensive layers to counter jailbreak and bias.
• Explicit domain-expert mapping for specialized tasks.
• Hierarchical Top-K allocation for adaptive resource usage.

Key Defensive Experts

1. Input Filtering
2. Attack Detection
3. Intent Analysis
4. Contextual Analysis
5. Safe Response Generation
6. Advanced Detection
7. Bias Mitigation
8. Output Sanitization

Mathematical Contributions

Three equations model the expert selection, hierarchical allocation, and weighted fusion processes.

Potential Applications

• Secure AI systems
• Medical and legal assistants
• Edge AI and IoT

Re-publication Note

This PDF was originally published on Zenodo. It is re-published here for archival and citation purposes. The Zenodo record remains the primary source.

Original DOI: 10.5281/zenodo.21853563
Original Date: 8 August 2026

Citation

@misc{tarif2026omoe,
  title={O-MoE: Optimized Mixture of Experts for Defensive and Specialized AI Systems},
  author={Tarif, Mouad},
  year={2026},
  doi={10.5281/zenodo.21853563}
}