这篇论文提出DYNA，用时间知识图给LLM加外挂记忆，不用重新训练就能减少遗忘，比微调和RAG都更准，适合看重时效性的应用。
DYNA是一个轻量级框架，通过整合时间知识图来增强冻结的LLM。该图将事件作为节点、时间关系作为带时间戳的有向边，作为外部可更新记忆。在查询时，DYNA通过随机游走和中心性度量检索相关节点，然后增强LLM的响应。在三个时间回忆任务上，DYNA相比微调减少约7%的灾难性遗忘，相比标准RAG提升约5%的时间排序准确性。实验发现，更高的图聚类系数与更好的检索性能相关。
DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning
Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining. We propose DYNA, a lightweight framework that augments a frozen LLM with a temporal knowledge graph where events are nodes and temporal relations are directed, timestamped edges. The graph serves as an external, updatable memory. At query time, DYNA retrieves relevant nodes via random walks and centrality measures, then augments the LLM's response. Evaluated on three temporal recall tasks, DYNA reduces catastrophic forgetting by ~7% compared to fine-tuning and improves temporal ordering by ~5% over standard RAG. Higher graph clustering coefficients correlate with better retrieval, showing that graph structure matters. Contributions: (1) episodic memory as temporal KG, (2) retraining-free LLM augmentation, (3) graph properties as predictors of retrieval performance.