[2603.01160] Semantic XPath: Structured Agentic Memory Access for Conversational AI

Computer Science > Artificial Intelligence arXiv:2603.01160 (cs) [Submitted on 1 Mar 2026] Title:Semantic XPath: Structured Agentic Memory Access for Conversational AI Authors:Yifan Simon Liu, Ruifan Wu, Liam Gallagher, Jiazhou Liang, Armin Toroghi, Scott Sanner View a PDF of the paper titled Semantic XPath: Structured Agentic Memory Access for Conversational AI, by Yifan Simon Liu and 5 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2603.01160 [cs.AI]   (or arXiv:2603.01160v1 [cs.AI] for this versi...