Structured memory representations such as knowledge graphs are central to autonomous agents and other long-lived systems. However, most existing approaches model time as discrete metadata, either sorting by recency (burying old-yet-permanent knowledge), simply overwriting outdated facts, or requiring an expensive LLM call at every ingestion step, leaving them unable to distinguish persistent facts from evolving ones. To address this, we introduce RoMem, a drop-in temporal knowledge graph module for structured memory systems, applicable to agentic memory and beyond. A pretrained Semantic Speed Gate maps each relation's text embedding to a volatility score, learning from data that evolving relations (e.g., "president of") should rotate fast while persistent ones (e.g., "born in") should remain stable. Combined with continuous phase rotation, this enables geometric shadowing: obsolete facts are rotated out of phase in complex vector space, so temporally correct facts naturally outrank contradictions without deletion. On temporal knowledge graph completion, RoMem achieves state-of-the-art results on ICEWS05-15 (72.6 MRR). Applied to agentic memory, it delivers 2-3x MRR and answer accuracy on temporal reasoning (MultiTQ), dominates hybrid benchmark (LoCoMo), preserves static memory with zero degradation (DMR-MSC), and generalises zero-shot to unseen financial domains (FinTMMBench).

翻译：结构化记忆表示（如知识图谱）是自主智能体及长期运行系统的核心。然而，现有方法大多将时间建模为离散元数据：要么按时间顺序排序（埋没陈旧但持久的知识），直接覆盖过时事实，或在每次摄取步骤中调用昂贵的语言模型——使得它们无法区分持久事实与演变事实。为此，我们提出RoMem——一种即插即用的时序知识图谱模块，适用于结构化记忆系统（可拓展至智能体记忆等领域）。预训练的语义速度门控根据关系文本嵌入映射出波动性得分，从数据中学习：演变关系（如"总统"）应快速旋转，而持久关系（如"出生地"）应保持稳定。结合连续相位旋转，该方法实现了几何阴影效应：过时事实在复数向量空间中被旋转出相位，从而使时间正确的事实无需删除即可自然压制矛盾项。在时序知识图谱补全任务中，RoMem在ICEWS05-15数据集上取得72.6 MRR的最佳结果。应用于智能体记忆时，其在时序推理（MultiTQ）中实现2-3倍MRR与答案准确率提升，主导混合基准（LoCoMo），保持静态记忆零退化（DMR-MSC），并零样本泛化至未知金融领域（FinTMMBench）。