Long-term conversational large language model (LLM) agents require memory systems that can recover relevant evidence from historical interactions without overwhelming the answer stage with irrelevant context. However, existing memory systems, including hierarchical ones, still often rely solely on vector similarity for retrieval. It tends to produce bloated evidence sets: adding many superficially similar dialogue turns yields little additional recall, but lowers retrieval precision, increases answer-stage context cost, and makes retrieved memories harder to inspect and manage. To address this, we propose HiGMem (Hierarchical and LLM-Guided Memory System), a two-level event-turn memory system that allows LLMs to use event summaries as semantic anchors to predict which related turns are worth reading. This allows the model to inspect high-level event summaries first and then focus on a smaller set of potentially useful turns, providing a concise and reliable evidence set through reasoning, while avoiding the retrieval overhead that would be excessively high compared to vector retrieval. On the LoCoMo10 benchmark, HiGMem achieves the best F1 on four of five question categories and improves adversarial F1 from 0.54 to 0.78 over A-Mem, while retrieving an order of magnitude fewer turns. Code is publicly available at https://github.com/ZeroLoss-Lab/HiGMem.

翻译：长期对话型大语言模型（LLM）代理需要具备记忆系统，能够从历史交互中检索相关证据，同时避免在回答阶段引入无关上下文。然而，现有记忆系统（包括分层式系统）仍常仅依赖向量相似度进行检索，这容易产生臃肿的证据集：增加大量表面上相似的对话轮次虽未显著提升召回率，却降低了检索精度、增加了回答阶段的上下文成本，并使检索到的记忆更难以审查和管理。为此，我们提出HiGMem（分层式与LLM引导记忆系统），这是一种双层事件-轮次记忆系统，允许LLM将事件摘要作为语义锚点，预测哪些相关轮次值得阅读。该方法使模型可先审查高层事件摘要，再聚焦于更小规模的可能有用轮次，通过推理提供简洁且可靠的证据集，同时避免因检索开销远超向量检索而导致的效率问题。在LoCoMo10基准测试中，HiGMem在五类问题中的四类上取得最佳F1分数，并将对抗性F1从0.54提升至0.78（相较于A-Mem），同时检索的轮次数降低一个数量级。代码已开源在 https://github.com/ZeroLoss-Lab/HiGMem。