A Study on the Coupling Relationship Between AI Personalization and Long-Context Management

With the large-scale deployment of large language models (LLMs) in various intelligent assistants and agent systems, homogenized outputs and the loss of user preferences across sessions have become key bottlenecks restricting improvements in user experience. Personalization technology aims to adapt LLMs to the language habits, thinking patterns, and subjective preferences of different users, while long-context and agent memory management addresses the underlying technical problems of storing, filtering, and reusing temporal information for LLMs. Current academic research mostly treats the two directions as independent fields, and systematic reviews of their internal coupling relationship are relatively scarce. This study first defines the core concepts of LLM personalization and long-context memory management and systematically reviews the technical principles, advantages, and existing limitations of four personalization paradigms: prompt-engineering personalization, lightweight parameter fine-tuning, user-preference alignment, and dynamic user profiling. It then divides long-context and agent-memory technologies into five routes: native-window expansion, context pruning and compression, adaptive context extraction, hierarchical memory architectures, and external retrieval-augmented memory, and analyzes the applicable scenarios and technical shortcomings of each route. On this basis, this study reviews progress in four intersecting directions: preference-aware memory updating, structured persona-memory representation, long-horizon personalization evaluation benchmarks, and training-free collaborative control at inference time. It further analyzes the internal coupling mechanism through which the two directions empower each other and summarizes three core conflicts: the capacity-accuracy trade-off, the conflict between general governance rules and personalized characteristics, and the conflict between computational cost and personalization stability. The study also summarizes five research challenges: difficulty in adapting to the dynamic evolution of user preferences, failure to recall implicit preferences, memory errors and preference drift, fragmented evaluation systems, and difficulty balancing engineering implementation costs. It proposes subsequent research paths in six dimensions: cognitively inspired memory architectures, preference-aware context compression algorithms, multimode hybrid indexing and retrieval, unified long-horizon personalization evaluation protocols, memory-error verification mechanisms, and multimodal long-term personalization. The findings indicate that long-term personalized intelligent systems cannot optimize the personalization module or memory module in isolation. Their coupling relationship should serve as the underlying design logic to promote the iterative upgrading of LLMs toward adaptation to individual user characteristics and to provide a reference for the theoretical research and engineering development of long-term agents and personalized dialogue assistants.