智能体记性差？HiGram 把记忆做成层级图，更新时只改相关路径，省 token 还更准，论文里有基准对比。
现有图记忆方法把记忆都放进平面图，历史信息积累后检索会混入无关上下文。HiGram 提出粗到细的分层记忆结构，用上层节点和 MemoryUnits 减少检索噪声。它用查询和更新条件化的 MicroGraph 做路径级定位，再对单元内和单元间依赖做协调重写。在长期会话问答和冲突感知记忆评估基准上，HiGram 相比基线在答案质量和 token 效率上都有提升。
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
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.