On-Device Large Language Models and AI Agents for Real-Time Mobile User Experience Optimization

Abstract

The rapid advancement of artificial intelligence has enabled the deployment of large language models (LLMs) directly on mobile devices, transforming how users interact with their smartphones and tablets. This review examines the current state of on-device large language models (LLMs) and artificial intelligence (AI) agents designed for real-time mobile user experience (UX) optimization. The integration of natural language processing (NLP) capabilities into edge computing environments presents unique opportunities for personalized, privacy-preserving, and responsive mobile applications. This paper synthesizes recent developments in model compression techniques, efficient inference architectures, and AI-driven personalization strategies that enable sophisticated language understanding without cloud dependency. We explore how on-device LLMs facilitate context-aware assistance, predictive text generation, intelligent content recommendation, and adaptive interface design. The review also addresses critical challenges including computational constraints, energy efficiency, model accuracy trade-offs, and real-time performance requirements. By analyzing recent publications from 2019 to 2024, we identify emerging trends in mobile AI deployment, examine the technical innovations that make real-time language processing feasible on resource-constrained devices, and discuss future directions for enhancing mobile UX through intelligent on-device agents. Our findings suggest that the convergence of model optimization techniques and hardware acceleration is creating unprecedented opportunities for delivering sophisticated AI-powered experiences while maintaining user privacy and reducing latency.