Multimodal Foundation Models for Real-Time Human–AI Interaction in Intelligent Service Systems
Keywords:
multimodal foundation models, real-time interaction, intelligent service systems, edge computing, model governance, human–AI interaction, latency-sensitive inference, fairness, robustnessAbstract
The rapid integration of multimodal foundation models into intelligent service systems has fundamentally reconfigured the landscape of real-time human–AI interaction. These models, capable of processing and generating information across text, image, audio, and video modalities, offer unprecedented opportunities for creating fluid, context-aware interfaces that respond instantly to human input. However, deploying such models in latency-sensitive service environments introduces profound architectural, infrastructural, and governance challenges. This paper presents a comprehensive systems-level analysis of multimodal foundation models as the computational backbone of real-time interaction within intelligent service systems. We examine the structural trade-offs between model size, multimodal alignment, and inference latency, and explore how modular architectures, caching strategies, and edge–cloud hybrids can balance responsiveness with representational fidelity. The discussion extends to deployment infrastructure, including distributed inference pipelines, model compression techniques, and energy sustainability concerns, as well as to issues of robustness under distributional shift and fairness across diverse user populations. Through case illustrations drawn from healthcare, autonomous mobility, and customer service domains, we highlight the heterogeneity of real-time interaction requirements and the need for domain-specific adaptation strategies. Finally, we address governance frameworks, algorithmic accountability, and policy directions that must accompany the widespread adoption of these systems. The paper argues that realizing the full potential of multimodal foundation models for real-time human–AI interaction demands a holistic approach that merges advances in model design with careful consideration of system-level constraints and societal implications.
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