Cross-Domain User Memory Transfer for Adaptive Large Language Model Applications
Keywords:
large language models, user memory, cross-domain transfer, adaptive systems, governance, fairness, privacy, sociotechnical infrastructureAbstract
Large language model applications increasingly operate as adaptive sociotechnical services that infer user intent, retrieve contextual knowledge, and shape digital experiences across multiple domains. However, most current systems remain stateless with respect to durable user histories or confine personalization within narrow application boundaries. This paper examines cross-domain user memory transfer as an infrastructure-level challenge involving the extraction, representation, consolidation, retrieval, and governance of user-specific knowledge that can be reused across heterogeneous application contexts. The analysis focuses on structural trade-offs rather than on algorithmic fine-tuning alone. It discusses architectural separation between local sensing, remote inference, and policy enforcement, and it examines representational choices such as natural language memory, embedding-based profiles, and structured user models. The paper further addresses privacy, consent, fairness, robustness, and operational sustainability as first-order design constraints rather than as downstream compliance concerns. Cross-domain transfer can improve longitudinal coherence and reduce repetitive user effort, but it also creates new risks of contextual collapse, biased generalization, and heightened surveillance. A central argument is that memory systems must be governed as active institutional infrastructures with provenance, auditability, deletion rights, and domain-specific oversight. The paper develops this argument through conceptual analysis, architectural discussion, and cross-sectoral comparison, concluding with policy implications and a forward-looking research agenda for memory-aware large language model systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
