Current approaches to memory in Large Language Models (LLMs) predominantly rely on static Retrieval-Augmented Generation (RAG), which often results in scattered retrieval and fails to capture the structural dependencies required for complex reasoning. For autonomous agents, these passive and flat architectures lack the cognitive organization necessary to model the dynamic and associative nature of long-term interaction. To address this, we propose Structured Episodic Event Memory (SEEM), a hierarchical framework that synergizes a graph memory layer for relational facts with a dynamic episodic memory layer for narrative progression. Grounded in cognitive frame theory, SEEM transforms interaction streams into structured Episodic Event Frames (EEFs) anchored by precise provenance pointers. Furthermore, we introduce an agentic associative fusion and Reverse Provenance Expansion (RPE) mechanism to reconstruct coherent narrative contexts from fragmented evidence. Experimental results on the LoCoMo and LongMemEval benchmarks demonstrate that SEEM significantly outperforms baselines, enabling agents to maintain superior narrative coherence and logical consistency.

翻译：当前大型语言模型（LLM）中的记忆方法主要依赖于静态的检索增强生成（RAG），这通常导致检索结果分散，且难以捕捉复杂推理所需的结构化依赖关系。对于自主智能体而言，这些被动且扁平化的架构缺乏必要的认知组织能力，无法对长期交互的动态性与关联性进行建模。为解决此问题，我们提出了结构化情景事件记忆（SEEM），这是一个分层框架，它将用于关系事实的图记忆层与用于叙事推进的动态情景记忆层协同整合。基于认知框架理论，SEEM将交互流转化为由精确溯源指针锚定的结构化情景事件框架（EEF）。此外，我们引入了一种智能体关联融合与反向溯源扩展（RPE）机制，以从碎片化证据中重建连贯的叙事上下文。在LoCoMo和LongMemEval基准测试上的实验结果表明，SEEM显著优于基线方法，使智能体能够保持更优的叙事连贯性与逻辑一致性。