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arXiv · 2509.18713

MemOrb: A Plug-and-Play Verbal-Reinforcement Memory Layer for E-Commerce Customer Service

Yizhe Huang·Yang Liu·Ruiyu Zhao·Xiaolong Zhong·Xingming Yue·Ling Jiang
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Abstract

Large Language Model-based agents(LLM-based agents) are increasingly deployed in customer service, yet they often forget across sessions, repeat errors, and lack mechanisms for continual self-improvement. This makes them unreliable in dynamic settings where stability and consistency are critical. To better evaluate these properties, we emphasize two indicators: task success rate as a measure of overall effectiveness, and consistency metrics such as Pass$^k$ to capture reliability across multiple trials. To address the limitations of existing approaches, we propose MemOrb, a lightweight and plug-and-play verbal reinforcement memory layer that distills multi-turn interactions into compact strategy reflections. These reflections are stored in a shared memory bank and retrieved to guide decision-making, without requiring any fine-tuning. Experiments show that MemOrb significantly improves both success rate and stability, achieving up to a 63 percentage-point gain in multi-turn success rate and delivering more consistent performance across repeated trials. Our results demonstrate that structured reflection is a powerful mechanism for enhancing long-term reliability of frozen LLM agents in customer service scenarios.

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Yizhe Huang, Yang Liu, Ruiyu Zhao, Xiaolong Zhong, Xingming Yue, Ling Jiang. 2025-09-23. MemOrb: A Plug-and-Play Verbal-Reinforcement Memory Layer for E-Commerce Customer Service. https://arxiv.org/abs/2509.18713

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