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.

翻译：[translated abstract in Chinese] 大语言模型（LLM）代理日益依赖外部记忆来支持长期交互、个性化辅助和多步推理。然而，现有记忆系统仍面临三个核心挑战：它们往往过度依赖语义相似性，从而可能遗漏对理解用户至关重要的证据；它们常将相关经历存储为孤立的片段，削弱了时间与因果连贯性；此外，它们通常采用静态记忆粒度，难以适应不同问题的需求。我们提出AdaMem——一种用于长期对话代理的自适应以用户为中心的记忆框架。AdaMem将对话历史组织为工作记忆、情景记忆、人物记忆和图记忆，使系统能在统一框架中保留近期上下文、结构化长期经历、稳定用户特征以及关系感知连接。在推理时，AdaMem首先解析目标参与者，然后构建基于问题的检索路径——该路径仅在必要时结合语义检索与关系感知图扩展——最终通过面向角色的流水线进行证据合成并生成答案。我们在LoCoMo和PERSONAMEM基准上评估AdaMem在长期推理与用户建模中的表现。实验结果表明，AdaMem在两个基准上均取得最先进水平。相关代码将在论文接收后公开。