Large language models (LLMs) are increasingly used in personalized applications, where responses must align with individual user preferences, histories, and profiles. A common approach is to inject user information into the prompt at inference time. However, existing methods typically rely on flat profile representations and similarity-based Top-K retrieval, which often introduce noisy or marginally relevant context under limited prompt budgets. In this paper, we propose PersMem, a user profile memory framework for LLM personalization that addresses three key questions: what user information to store, how to organize it, and how to use it effectively during generation. First, we construct a structured user profile memory by extracting entities and concepts from user profiles. Second, we organize the memory into a hierarchical structure that separates short-term interactions from long-term knowledge, enabling scalable and coarse-to-fine retrieval. Third, we introduce a learning-based memory gating mechanism that adaptively selects long-term memory units under prompt budget constraints and directly optimizes answer quality through evaluated signals. Extensive experiments on multiple personalization tasks demonstrate that our approach consistently improves personalization effectiveness while reducing prompt length, compared with existing prompt-based and retrieval-based baselines.
Yang-Xu Liao, Yongheng Deng, Tianyuan Jiang et al.· Proceedings of the 32nd ACM...· 0 citations
Retrieval-Augmented Generation (RAG) grounds large language models in external knowledge and has become a key technique for knowledge-intensive tasks. As knowledge bases continue to scale, however, the retrieval stage increasingly dominates end-to-end latency, limiting the responsiveness of RAG systems. In this paper, we identify and empirically validate a previously underexplored property of RAG workloads: strong per-user query locality, where individual users' queries concentrate on a small subset of the knowledge space. Motivated by this observation, we propose Lever, a locality-aware collaborative retrieval framework that exploits query locality to accelerate graph-based RAG retrieval. Lever maintains compact, personalized subgraph indexes on user's local devices as auxiliary structures to guide retrieval toward semantically relevant regions of a global index, enabling more efficient graph traversal without sacrificing coverage. To sustain effectiveness over time, Lever further incorporates adaptive resampling mechanisms that align on-device indexes with evolving query patterns. Extensive experiments on multiple RAG benchmarks demonstrate that Lever significantly reduces retrieval latency and improves throughput while preserving retrieval quality, highlighting query locality as a powerful and complementary lever for scalable RAG retrieval.
Yongheng Deng, Tianyuan Jiang, Zhenya Ma et al.· Proceedings of the 32nd ACM...· 0 citations
PersMem is proposed, a user profile memory framework for LLM personalization that addresses three key questions: what user information to store, how to organize it, and how to use it effectively during generation that consistently improves personalization effectiveness while reducing prompt length.
Yang-Xu Liao, Yongheng Deng, Tianyuan Jiang et al.· Proceedings of the 32nd ACM...· 0 citations
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