A Hierarchical AI-OS Framework for Multi-Modal Grounding and Privacy-Aware Processing

Modern operating systems (OS) must effectively manage multi-modal grounding, rapid user interface adaptation, and the trade-offs between privacy and computational efficiency when integrating artificial intelligence (AI). To address these challenges, we propose a hierarchical agent framework that seamlessly combines multi-modal fusion, reinforcement learning (RL)-based task planning, and a privacy-preserving edge-cloud hybrid architecture. The framework incorporates a Proximal Policy Optimization (PPO) scheduler for intelligent task decomposition, a sensitivity-aware task routing mechanism, and a cross-modal attention module for accurate user intent interpretation. Experiments conducted on the OS-Copilot benchmark demonstrate significant improvements, including a 30 % enhancement in privacy preservation, a 40 % reduction in latency, and a 92 % task success rate compared to state-of-the-art baselines. Ablation studies further validate the individual contributions of each system component. Recognizing current limitations such as energy consumption and API dependency, we outline future directions for scalable AI-OS integration, including federated learning, synthetic data augmentation, and energyefficient model optimization.