用树状结构记忆提升对话智能体长期任务表现。
Semantic XPath: Structured Agentic Memory Access for Conversational AI
- 设计树形记忆模块，支持结构化信息的精准访问与更新。
- 性能比扁平检索提升176.7%，仅需原始上下文9.1%的token。
- 适合长期任务型对话系统研发者与架构设计参考。
对话智能体（ConvAI）越来越依赖结构化记忆以支持长期、任务导向的交互。基于上下文的记忆方法将历史记录不断拼接至模型输入，受限于上下文窗口而扩展性差；基于检索增强生成（RAG）的方法虽能高效获取信息，但多数假设记忆为扁平集合，忽视了结构。本文提出语义XPath（Semantic XPath），一种树状结构记忆模块，可实现对结构化对话记忆的精准访问与更新。实验表明，该方法在性能上相较扁平RAG基线提升176.7%，同时仅需传统上下文记忆方法9.1%的token。我们还构建了端到端的SemanticXPath Chat系统，可视化展示结构化记忆及查询执行过程。整体上，本工作为下一代基于结构化记忆的长期任务型对话系统提供了可行范式。
原文摘要 · Abstract (English)
Conversational AI (ConvAI) agents increasingly maintain structured memory to support long-term, task-oriented interactions. In-context memory approaches append the growing history to the model input, which scales poorly under context-window limits. RAG-based methods retrieve request-relevant information, but most assume flat memory collections and ignore structure. We propose Semantic XPath, a tree-structured memory module to access and update structured conversational memory. Semantic XPath improves performance over flat-RAG baselines by 176.7% while using only 9.1% of the tokens required by in-context memory. We also introduce SemanticXPath Chat, an end-to-end ConvAI demo system that visualizes the structured memory and query execution details. Overall, this paper demonstrates a candidate for the next generation of long-term, task-oriented ConvAI systems built on structured memory.
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