Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory

2026-01-03

arXiv.org

AI Summary

Existing studies offer conflicting conclusions regarding the effectiveness of graph-based structures in dialogue memory systems, making it difficult to isolate key design factors. This work proposes the first unified and modular framework for analyzing dialogue memory mechanisms, accommodating both graph-based and non-graph approaches. Through staged controlled experiments on LongMemEval and HaluMem, the study systematically evaluates the impact of core components—memory representation, organization, maintenance, and retrieval. Findings reveal that performance differences primarily stem from underlying system configurations rather than specific architectural innovations. Moreover, the analysis identifies several robust and consistently strong baselines, establishing reproducible benchmarks and actionable design guidelines for future research in dialogue memory systems.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Graph structures are increasingly used in dialog memory systems, but empirical findings on their effectiveness remain inconsistent, making it unclear which design choices truly matter. We present an experimental, system-oriented analysis of long-term dialog memory architectures. We introduce a unified framework that decomposes dialog memory systems into core components and supports both graph-based and non-graph approaches. Under this framework, we conduct controlled, stage-wise experiments on LongMemEval and HaluMem, comparing common design choices in memory representation, organization, maintenance, and retrieval. Our results show that many performance differences are driven by foundational system settings rather than specific architectural innovations. Based on these findings, we identify stable and reliable strong baselines for future dialog memory research. Code are available at https://github.com/AvatarMemory/UnifiedMem

Problem

• dialog memory
• graph structures
• long-term memory
• empirical analysis
• memory architecture

Innovation

• dialog memory
• unified framework
• graph-based memory
• empirical analysis
• long-term dialogue