@inproceedings{liu-etal-2026-semantic,
title = "Semantic {XP}ath: Structured Agentic Memory Access for Conversational {AI}",
author = "Liu, Yifan Simon and
Wu, Ruifan and
Gallagher, Liam and
Liang, Jiazhou and
Toroghi, Armin and
Sanner, Scott",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.28/",
pages = "286--296",
ISBN = "979-8-89176-392-0",
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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%0 Conference Proceedings
%T Semantic XPath: Structured Agentic Memory Access for Conversational AI
%A Liu, Yifan Simon
%A Wu, Ruifan
%A Gallagher, Liam
%A Liang, Jiazhou
%A Toroghi, Armin
%A Sanner, Scott
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F liu-etal-2026-semantic
%X 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.
%U https://aclanthology.org/2026.acl-demo.28/
%P 286-296
Markdown (Informal)
[Semantic XPath: Structured Agentic Memory Access for Conversational AI](https://aclanthology.org/2026.acl-demo.28/) (Liu et al., ACL 2026)
ACL
- Yifan Simon Liu, Ruifan Wu, Liam Gallagher, Jiazhou Liang, Armin Toroghi, and Scott Sanner. 2026. Semantic XPath: Structured Agentic Memory Access for Conversational AI. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 286–296, San Diego, California, United States. Association for Computational Linguistics.