让大模型长时记忆更高效，通过分层图结构提升检索与更新速度。
LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning
- 用实体和关系构建分层图结构，分离语义与拓扑，避免冗余。
- 在长对话任务中，推理准确率提升18%，更新延迟降低40%。
- 适合需要长期一致性的对话系统、智能助手等场景使用。
大语言模型代理具备出色的对话与推理能力，但受限于有限的上下文窗口和缺乏持久记忆。现有外部记忆架构多采用图结构，但普遍为扁平且纠缠的拓扑形式，导致语义与结构混杂，引发冗余表示、无序检索，影响效率与准确性。为此，我们提出 LiCoMemory，一个端到端的代理记忆框架，支持实时更新与检索。其核心是 CogniGraph——一种轻量级分层图结构，以实体与关系作为语义索引层，并结合时间与层次感知的搜索机制及集成重排序，实现自适应、连贯的知识检索。在长时对话基准 LoCoMo 与 LongMemEval 上的实验表明，LiCoMemory 不仅在时间推理、多会话一致性与检索效率方面优于主流基线，还显著降低更新延迟。官方代码与数据已开源：https://github.com/EverM0re/LiCoMemory。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents exhibit remarkable conversational and reasoning capabilities but remain constrained by limited context windows and the lack of persistent memory. Recent efforts address these limitations via external memory architectures, often employing graph-based representations, yet most adopt flat, entangled structures that intertwine semantics with topology, leading to redundant representations, unstructured retrieval, and degraded efficiency and accuracy. To resolve these issues, we propose LiCoMemory, an end-to-end agentic memory framework for real-time updating and retrieval, which introduces CogniGraph, a lightweight hierarchical graph that utilizes entities and relations as semantic indexing layers, and employs temporal and hierarchy-aware search with integrated reranking for adaptive and coherent knowledge retrieval. Experiments on long-term dialogue benchmarks, LoCoMo and LongMemEval, show that LiCoMemory not only outperforms established baselines in temporal reasoning, multi-session consistency, and retrieval efficiency, but also notably reduces update latency. Our official code and data are available at https://github.com/EverM0re/LiCoMemory.
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