arXiv:2606.27005v1 Announce Type: new Abstract: Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties such as fairness and explainability. This paper presents AURORA-AI, an Adaptive Utility-driven Resource Orchestration framework for Resilient AI that unifies Hamilton-Jacobi-Bellman feedback control, Lyapunov-based stability monitoring, and a fairness-aware composite utility into a single closed-loop policy.The framewo
This publication appears as AI systems face increasing challenges in real-world deployment due to non-stationary conditions, necessitating robust resource orchestration solutions.
Adaptive resource orchestration is critical for ensuring resilient, fair, and explainable AI in dynamic environments, directly impacting the reliability and trustworthiness of future AI applications.
This framework offers a method to unify control theory and fairness-aware utility for AI resource management, moving beyond static allocation strategies.
- · AI system developers
- · Cloud infrastructure providers
- · Sectors reliant on critical AI deployments
- · Traditional static resource allocation methods
- · AI systems lacking adaptive resilience
Improved reliability and performance of AI systems in production environments.
Accelerated adoption of complex AI in critical infrastructure and public services due to enhanced resilience.
Increased regulatory focus on adaptive and explainable AI resource management as a standard for deployment.
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Read at arXiv cs.AI