Research Paper#Generative AI, Operations Research, Assured Autonomy, Safety, Reliability🔬 ResearchAnalyzed: Jan 3, 2026 16:53
Assured Autonomy in GenAI: An Operations Research Approach
Published:Dec 30, 2025 04:24
•1 min read
•ArXiv
Analysis
This paper addresses the growing autonomy of Generative AI (GenAI) systems and the need for mechanisms to ensure their reliability and safety in operational domains. It proposes a framework for 'assured autonomy' leveraging Operations Research (OR) techniques to address the inherent fragility of stochastic generative models. The paper's significance lies in its focus on the practical challenges of deploying GenAI in real-world applications where failures can have serious consequences. It highlights the shift in OR's role from a solver to a system architect, emphasizing the importance of control logic, safety boundaries, and monitoring regimes.
Key Takeaways
- •GenAI systems require mechanisms for assured autonomy as they gain operational autonomy.
- •Operations Research (OR) provides a framework for building reliable and safe GenAI systems.
- •The framework uses flow-based generative models and an adversarial robustness lens.
- •OR's role shifts from solver to system architect in the context of increasing autonomy.
Reference
“The paper argues that 'stochastic generative models can be fragile in operational domains unless paired with mechanisms that provide verifiable feasibility, robustness to distribution shift, and stress testing under high-consequence scenarios.'”