Memory-Augmented Multimodal Agents for Personalized Human-Computer Interaction

Abstract

Memory-augmented multimodal agents represent a convergence of persistent computational memory, multimodal perception, and personalized interaction design. This paper examines the system-level architecture, infrastructure, governance, and socio-technical implications of such agents for human-computer interaction. It argues that memory is not merely a feature but a structural layer that reshapes user modeling, context awareness, and trust. The discussion situates memory augmentation within longstanding HCI traditions while addressing contemporary advances in foundation models, reinforcement learning, and multimodal fusion. Particular attention is given to trade-offs among personalization, privacy, robustness, fairness, and sustainability. The paper considers deployment contexts ranging from assistive computing and education to workplace productivity and everyday digital services. It also analyzes governance mechanisms, including consent, transparency, auditability, and policy frameworks. Evaluation challenges are discussed, including long-horizon metrics, adversarial robustness, and cross-cultural validity. Through case illustrations and cross-domain comparisons, the paper shows that memory-augmented multimodal agents can improve continuity and reduce interaction friction, but they also concentrate power, create dependency, and raise difficult questions about data ownership and identity. A balanced research agenda is proposed that treats memory as a governed infrastructure rather than an invisible convenience. The conclusion emphasizes that technical optimization must be paired with institutional accountability and participatory design.