ES-Mem: Event Segmentation-Based Memory for Long-Term Dialogue Agents

• URL: http://arxiv.org/abs/2601.07582v2
• Date: Tue, 13 Jan 2026 15:04:26 GMT
• Title: ES-Mem: Event Segmentation-Based Memory for Long-Term Dialogue Agents
• Authors: Huhai Zou, Tianhao Sun, Chuanjiang He, Yu Tian, Zhenyang Li, Li Jin, Nayu Liu, Jiang Zhong, Kaiwen Wei,
• Abstract summary: ES-Mem is a framework that partitions long-term interactions into semantically coherent events with distinct boundaries.<n>We show that ES-Mem yields consistent performance gains over baseline methods.<n>The proposed event segmentation module exhibits robust applicability on dialogue segmentation datasets.
• Score: 25.10969436399974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory is critical for dialogue agents to maintain coherence and enable continuous adaptation in long-term interactions. While existing memory mechanisms offer basic storage and retrieval capabilities, they are hindered by two primary limitations: (1) rigid memory granularity often disrupts semantic integrity, resulting in fragmented and incoherent memory units; (2) prevalent flat retrieval paradigms rely solely on surface-level semantic similarity, neglecting the structural cues of discourse required to navigate and locate specific episodic contexts. To mitigate these limitations, drawing inspiration from Event Segmentation Theory, we propose ES-Mem, a framework incorporating two core components: (1) a dynamic event segmentation module that partitions long-term interactions into semantically coherent events with distinct boundaries; (2) a hierarchical memory architecture that constructs multi-layered memories and leverages boundary semantics to anchor specific episodic memory for precise context localization. Evaluations on two memory benchmarks demonstrate that ES-Mem yields consistent performance gains over baseline methods. Furthermore, the proposed event segmentation module exhibits robust applicability on dialogue segmentation datasets.

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