AI 中文总结
本文提出具备路径级定位与重写能力的分层图记忆框架HiGram，解决扁平图记忆的无关信息多、重写效率低问题，在相关基准上提升了答案质量、令牌效率及冲突场景下的表现。
AI 中文摘要
用于长期推理的智能体需要一种能随新事实与外部反馈持续高效且有效更新的记忆机制。近期，图记忆已被采用以提供多跳检索与推理的结构化组织方式，但现有方法将所有记忆存储于扁平图中，累积的历史记忆会引入无关上下文并增加检索时的证据选择成本；此外，这类方法通常独立更新记忆单元，需重复进行单元级重写以覆盖相关变更。为解决这些问题，本文提出HiGram，一种具备路径级定位与重写能力的演进式分层图记忆框架。具体而言，我们首先提出分层图记忆，将记忆组织为由上层节点与MemoryUnits（记忆单元）构成的粗到细架构，从而减少检索过程中的无关信息量；我们进一步提出基于MicroGraph（微图）的路径级定位，利用查询与更新条件化的MicroGraph在重写前识别支持子图与证据路径；最后，我们提出一种协同重写方法，该方法联合修正单元内记忆与单元间依赖，使局部证据路径中的有效依赖结构得以更新。在长期对话问答与冲突感知记忆评估基准上的实验表明，我们的方法在答案质量与令牌效率上较基线方法取得显著提升，此外，我们的方法在动态、静态及条件冲突场景下均提升了答案准确率与查询有效证据选择能力。
英文摘要
Agents for long term reasoning require a memory that can be efficiently and effectively updated over time, as new facts and external feedback continue to arrive. Recently, graph memory has been adopted to offer structural organization for multi-hop retrieval and reasoning. However, existing methods store all memories in a flat graph, and accumulated historical memories can introduce irrelevant contexts and increase the cost of evidence selection during retrieval. Moreover, they typically update memory units independently, requiring repeated unit-wise rewrite to cover related changes. To address these issues, we propose HiGram, an evolving hierarchical graph memory framework with path-level localization and rewriting. Specifically, we first propose a hierarchical graph memory, which organizes the memory into coarse-to-fine architecture composed of upper-level nodes and MemoryUnits, thereby reducing the amount of irrelevant information during retrieval. We further propose MicroGraph-based path-level localization, which leverages query and update conditioned MicroGraphs to identify support subgraph and evidence path before rewrite. Finally, we propose a coordinated rewriting method that jointly revises intra-unit memory and inter-unit dependencies, enable valid dependency structures updating in the localized evidence path. Experiments on benchmarks for long-term conversational question answering and conflict-aware memory evaluation demonstrate that our method demonstrate substantial improvements over baselines in answer quality and token efficiency. Besides, our method improves answer accuracy and query-valid evidence selection under dynamic, static, and conditional conflicts.
