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arXiv · 2603.01160

Semantic XPath: Structured Agentic Memory Access for Conversational AI

Yifan Simon Liu·Ruifan Wu·Liam Gallagher·Jiazhou Liang·Armin Toroghi·Scott Sanner
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Abstract

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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Yifan Simon Liu, Ruifan Wu, Liam Gallagher, Jiazhou Liang, Armin Toroghi, Scott Sanner. 2026-03-01. Semantic XPath: Structured Agentic Memory Access for Conversational AI. https://arxiv.org/abs/2603.01160

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