MemEmo: Evaluating Emotion in Memory Systems of Agents

• URL: http://arxiv.org/abs/2602.23944v1
• Date: Fri, 27 Feb 2026 11:46:08 GMT
• Title: MemEmo: Evaluating Emotion in Memory Systems of Agents
• Authors: Peng Liu, Zhen Tao, Jihao Zhao, Ding Chen, Yansong Zhang, Cuiping Li, Zhiyu Li, Hong Chen,
• Abstract summary: We propose an emotion-enhanced memory evaluation benchmark to assess the performance of memory systems in handling affective information.<n>We developed the textbfHuman-textbfLike textbfMemory textbfEmotion (textbfHLME) dataset, which evaluates memory systems across three dimensions.<n> Experimental results indicate that none of the evaluated systems achieve robust performance across all three tasks.
• Score: 30.12443912702673
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Memory systems address the challenge of context loss in Large Language Model during prolonged interactions. However, compared to human cognition, the efficacy of these systems in processing emotion-related information remains inconclusive. To address this gap, we propose an emotion-enhanced memory evaluation benchmark to assess the performance of mainstream and state-of-the-art memory systems in handling affective information. We developed the \textbf{H}uman-\textbf{L}ike \textbf{M}emory \textbf{E}motion (\textbf{HLME}) dataset, which evaluates memory systems across three dimensions: emotional information extraction, emotional memory updating, and emotional memory question answering. Experimental results indicate that none of the evaluated systems achieve robust performance across all three tasks. Our findings provide an objective perspective on the current deficiencies of memory systems in processing emotional memories and suggest a new trajectory for future research and system optimization.

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