AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents

Shannan Yan, Jingchen Ni, Leqi Zheng, zjj, Peixi Wu, Dacheng Yin, Jing Lyu, Chun Yuan, Fengyun Rao

AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents: 13 upvotes on Hugging Face Daily Papers, #14 of 33 papers on 2026-03-19. Day-by-day upvote history.

Large language model (LLM) agents increasingly rely on external memory to support long-horizon interaction, personalized assistance, and multi-step reasoning. However, existing memory systems still face three core challenges: they often rely too heavily on semantic similarity, which can miss evidence crucial for user-centric understanding; they frequently store related experiences as isolated fragments, weakening temporal and causal coherence; and they typically use static memory granularities that do not adapt well to the requirements of different questions. We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents. AdaMem organizes dialogue history into working, episodic, persona, and graph memories, enabling the system to preserve recent context, structured long-term experiences, stable user traits, and relation-aware connections within a unified framework. At inference time, AdaMem first resolves the target participant, then builds a question-conditioned retrieval route that combines semantic retrieval with relation-aware graph expansion only when needed, and finally produces the answer through a role-specialized pipeline for evidence synthesis and response generation. We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling. Experimental results show that AdaMem achieves state-of-the-art performance on both benchmarks. The code will be released upon acceptance.

Paper page on Hugging Face · arXiv

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