# Semantic XPath: Structured Agentic Memory Access for Conversational AI

arXiv: https://arxiv.org/abs/2603.01160 · HTML: https://mlsanity.com/paper/2603.01160/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 16 of 16)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading](https://mlsanity.com/paper/2310.05029/) (2023)
- [MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation](https://mlsanity.com/paper/2308.08239/) (2023)
- [From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs](https://mlsanity.com/paper/2410.14052/) (2024)
- [Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents](https://mlsanity.com/paper/2507.22925/) (2025)
- [CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension](https://mlsanity.com/paper/2510.05520/) (2025)
- [Selective Attention: Enhancing Transformer through Principled Context Control](https://mlsanity.com/paper/2411.12892/) (2024)
- [Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendation](https://mlsanity.com/paper/2410.01598/) (2024)
- [MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval](https://mlsanity.com/paper/2509.13562/) (2025)
- [Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments](https://mlsanity.com/paper/2510.22023/) (2025)
- [A Simple but Effective Elaborative Query Reformulation Approach for Natural Language Recommendation](https://mlsanity.com/paper/2510.02656/) (2025)

## Built on by (most cited first, 7 of 7)

- [Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation](https://mlsanity.com/paper/2604.09698/) (2026)
- [Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems](https://mlsanity.com/paper/2605.12213/) (2026)
- [SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents](https://mlsanity.com/paper/2606.28356/) (2026)
- [Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents](https://mlsanity.com/paper/2606.04555/) (2026)
- [MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations](https://mlsanity.com/paper/2608.15844/) (2026)
- [The Missing Complement: State-Conditioned Minimal Sufficient Evidence for Coding Agents](https://mlsanity.com/paper/2609.20050/) (2026)
- [Have I Scene This Before? Spatially Grounded Conversational Memory for Complex Queries in Egocentric Assistants](https://mlsanity.com/paper/2610.05526/) (2026)

## Abstract (arXiv)

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.