@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://preview.aclanthology.org/ingest-acl/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."
}