Semantic XPath: Structured Agentic Memory Access for Conversational AI
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 vi...
| Authors | Yifan Simon Liu, Ruifan Wu, Liam Gallagher et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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