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

Abstract

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 > **Open for Collaboration:** This framework is ready for empirical testing. Researchers interested in implementing or validating O-MoE are welcome to contact the author at: mouad.tarif@inbox.ru