@inproceedings{fu-etal-2026-enpmr,
title = "{ENPMR}-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents",
author = "Fu, Xing and
Hu, Yulin and
Ji, Mengtong and
Li, Haozhen and
Sun, Yixin and
Zhao, Weixiang and
Zhao, Yanyan and
Qin, Bing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2080/",
pages = "41910--41933",
ISBN = "979-8-89176-395-1",
abstract = "Memory-augmented language agents are increasingly deployed in affective applications such as emotional support, where understanding and responding to users' latent emotional needs is critical. However, existing research often treats memory as a tool for factual retrieval, overlooking its role in shaping users' emotional experiences. In this work, we introduce ENPMR-Bench, a benchmark for evaluating Emotional Need-aware Proactive Memory Retrieval (ENPMR), a core capability that enables agents to infer users' latent emotional needs and proactively retrieve appropriate memories to support empathetic interaction. Grounded in Maslow{'}s hierarchy of needs, ENPMR-Bench includes over 1,800 memory-augmented dialogues and defines structured mappings between emotional needs and supportive memory types. Experimental results demonstrate that current retrieval paradigms, including both embedding-based and LLM-driven approaches, exhibit substantial deficiencies, with empathy scores significantly lagging behind golden memory conditions. While chain-of-thought prompting improves the alignment between inferred emotional needs and retrieved memories to some extent, a notable performance gap remains. Together, these findings reveal critical limitations in current agents and outline directions for advancing personalized emotional support through need-sensitive memory retrieval."
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<abstract>Memory-augmented language agents are increasingly deployed in affective applications such as emotional support, where understanding and responding to users’ latent emotional needs is critical. However, existing research often treats memory as a tool for factual retrieval, overlooking its role in shaping users’ emotional experiences. In this work, we introduce ENPMR-Bench, a benchmark for evaluating Emotional Need-aware Proactive Memory Retrieval (ENPMR), a core capability that enables agents to infer users’ latent emotional needs and proactively retrieve appropriate memories to support empathetic interaction. Grounded in Maslow’s hierarchy of needs, ENPMR-Bench includes over 1,800 memory-augmented dialogues and defines structured mappings between emotional needs and supportive memory types. Experimental results demonstrate that current retrieval paradigms, including both embedding-based and LLM-driven approaches, exhibit substantial deficiencies, with empathy scores significantly lagging behind golden memory conditions. While chain-of-thought prompting improves the alignment between inferred emotional needs and retrieved memories to some extent, a notable performance gap remains. Together, these findings reveal critical limitations in current agents and outline directions for advancing personalized emotional support through need-sensitive memory retrieval.</abstract>
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%0 Conference Proceedings
%T ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents
%A Fu, Xing
%A Hu, Yulin
%A Ji, Mengtong
%A Li, Haozhen
%A Sun, Yixin
%A Zhao, Weixiang
%A Zhao, Yanyan
%A Qin, Bing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F fu-etal-2026-enpmr
%X Memory-augmented language agents are increasingly deployed in affective applications such as emotional support, where understanding and responding to users’ latent emotional needs is critical. However, existing research often treats memory as a tool for factual retrieval, overlooking its role in shaping users’ emotional experiences. In this work, we introduce ENPMR-Bench, a benchmark for evaluating Emotional Need-aware Proactive Memory Retrieval (ENPMR), a core capability that enables agents to infer users’ latent emotional needs and proactively retrieve appropriate memories to support empathetic interaction. Grounded in Maslow’s hierarchy of needs, ENPMR-Bench includes over 1,800 memory-augmented dialogues and defines structured mappings between emotional needs and supportive memory types. Experimental results demonstrate that current retrieval paradigms, including both embedding-based and LLM-driven approaches, exhibit substantial deficiencies, with empathy scores significantly lagging behind golden memory conditions. While chain-of-thought prompting improves the alignment between inferred emotional needs and retrieved memories to some extent, a notable performance gap remains. Together, these findings reveal critical limitations in current agents and outline directions for advancing personalized emotional support through need-sensitive memory retrieval.
%U https://aclanthology.org/2026.findings-acl.2080/
%P 41910-41933
Markdown (Informal)
[ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents](https://aclanthology.org/2026.findings-acl.2080/) (Fu et al., Findings 2026)
ACL
- Xing Fu, Yulin Hu, Mengtong Ji, Haozhen Li, Yixin Sun, Weixiang Zhao, Yanyan Zhao, and Bing Qin. 2026. ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41910–41933, San Diego, California, United States. Association for Computational Linguistics.