[论文解读] TA-Mem: Tool-Augmented Autonomous Memory Retrieval for LLM in Long-Term Conversational QA

Mengwei Yuan, Jianan Liu+5 more|arXiv (Cornell University)|Mar 10, 2026

Topic Modeling被引用 0

一句话总结

TA-Mem 引入了一种面向长期对话的工具增强型自治记忆检索框架，通过自适应记忆提取、多索引存储和工具驱动的检索来处理长期对话。它在 LoCoMo 上相对于基线显示出改进的性能，并分析了不同问题类型的工具使用情况。

ABSTRACT

Large Language Model (LLM) has exhibited strong reasoning ability in text-based contexts across various domains, yet the limitation of context window poses challenges for the model on long-range inference tasks and necessitates a memory storage system. While many current storage approaches have been proposed with episodic notes and graph representations of memory, retrieval methods still primarily rely on predefined workflows or static similarity top-k over embeddings. To address this inflexibility, we introduced a novel tool-augmented autonomous memory retrieval framework (TA-Mem), which contains: (1) a memory extraction LLM agent which is prompted to adaptively chuck an input into sub-context based on semantic correlation, and extract information into structured notes, (2) a multi-indexed memory database designed for different types of query methods including both key-based lookup and similarity-based retrieval, (3) a tool-augmented memory retrieval agent which explores the memory autonomously by selecting appropriate tools provided by the database based on the user input, and decides whether to proceed to the next iteration or finalizing the response after reasoning on the fetched memories. The TA-Mem is evaluated on the LoCoMo dataset, achieving significant performance improvements over existing baseline approaches. In addition, an analysis of tool use across different question types also demonstrates the adaptivity of the proposed method.

研究动机与目标

• 解决 LLM 在长期对话问答中的记忆与上下文窗口限制。
• 开发具备自适应提取、多类型索引、基于工具的检索的灵活记忆检索系统。
• 使通过工具实现对记忆的自治探索，以提升检索与应答生成。

提出的方法

• 引入一个记忆提取的 LLM 代理，能够自适应地将输入分块为语义相关的子上下文并提取结构化笔记。
• 设计一个多索引记忆数据库，支持基于键的查找和基于相似性的检索，以适应多样化的查询方式。
• 提出一个工具增强的记忆检索代理，能够根据用户输入和推理进展自主从数据库中选择合适的工具。

实验结果

研究问题

• RQ1 如何使基于 LLM 的长期问答的记忆检索比固定工作流或静态嵌入更具灵活性？
• RQ2 一个工具增强的记忆检索代理能否自治地遍历记忆以提升应答质量？
• RQ3 多索引记忆存储在长期对话任务的不同查询模式中有哪些好处？

主要发现

• TA-Mem 框架在 LoCoMo 数据集上相较现有基线实现显著性能提升。
• 对不同问题类型的工具使用分析展示了该方法的自适应性。
• 该框架将自适应记忆提取与自治工具驱动检索结合，提升了长期问答的能力。