Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments. To effectively scale to long-horizon scenarios, a key capability for such agents is a memory mechanism that can retain, organize, and retrieve past experiences to support downstream decision-making. However, most existing approaches organize and store memories in a flat manner and rely on simple similarity-based retrieval techniques. Even when structured memory is introduced, existing methods often struggle to explicitly capture the logical relationships among experiences or memory units. Moreover, memory access is largely detached from the constructed structure and still depends on shallow semantic retrieval, preventing agents from reasoning logically over long-horizon dependencies. In this work, we propose CompassMem, an event-centric memory framework inspired by Event Segmentation Theory. CompassMem organizes memory as an Event Graph by incrementally segmenting experiences into events and linking them through explicit logical relations. This graph serves as a logic map, enabling agents to perform structured and goal-directed navigation over memory beyond superficial retrieval, progressively gathering valuable memories to support long-horizon reasoning. Experiments on LoCoMo and NarrativeQA demonstrate that CompassMem consistently improves both retrieval and reasoning performance across multiple backbone models.

翻译：大型语言模型（LLM）正日益被部署为能够推理、规划并与环境交互的智能体。为了有效扩展到长周期场景，此类智能体的关键能力在于一种能够保留、组织并检索过往经验以支持下游决策的记忆机制。然而，现有方法大多以扁平方式组织和存储记忆，并依赖简单的基于相似性的检索技术。即使引入了结构化记忆，现有方法也往往难以显式捕捉经验或记忆单元间的逻辑关系。此外，记忆访问在很大程度上与所构建的结构脱节，仍依赖于浅层语义检索，导致智能体无法对长周期依赖关系进行逻辑推理。本研究提出 CompassMem，一种受事件分割理论启发的、以事件为中心的记忆框架。CompassMem 通过将经验逐步分割为事件，并通过显式逻辑关系将其链接，从而将记忆组织为事件图。该图作为逻辑图谱，使智能体能够超越浅层检索，在记忆上进行结构化、目标导向的导航，逐步收集有价值的记忆以支持长周期推理。在 LoCoMo 和 NarrativeQA 上的实验表明，CompassMem 在多种骨干模型中均能持续提升检索与推理性能。