Memory is a core component of conversational agents, enabling coherent and context-aware behavior over long interactions. Recent approaches commonly rely on LLM-based memory construction, where raw interactions are rewritten into structured memory units and later retrieved via a RAG pipeline. While effective in control...
Dong-Hua Cai, Yong-Heng Deng, Yi-Fei Wang et al.· 0 citations
This paper proposes Lever, a locality-aware collaborative retrieval framework that exploits query locality to accelerate graph-based RAG retrieval, and identifies and empirically validate a previously underexplored property of RAG workloads: strong per-user query locality.
Yong-Heng Deng, Tianyuan Jiang, Zhen-Ya Ma et al.· Proceedings of the 32nd ACM...· 0 citations
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 representatio...
Yang-Xu Liao, Yong-Heng 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,...
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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