@inproceedings{shi-etal-2026-mem,
title = "Mem-{ABC}: Attitude-Aware Agent Memory for Personalized Question Answering",
author = "Shi, Yinghan and
Fu, Kai and
Chu, Quanyou and
Liu, Xinyu and
Du, Ming and
Wang, Hongya and
Meng, Xiaojun and
Wei, Jiansheng and
Xiao, Yanghua and
Xu, Bo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1678/",
pages = "35815--35835",
ISBN = "979-8-89176-462-0",
abstract = "Personalized Question Answering (PQA) requires models to shift from a fact-centric to a user-centric paradigm, inferring implicit intents from historical interactions. However, standard Retrieval-Augmented Generation (RAG) struggles with narrative noise and fragmented semantics. Similarly, while Agent Memory was proposed to maintain long-term consistency, existing implementations predominantly focus on accumulating objective facts, neglecting the subjective attitudes essential for personalization. We argue that for PQA, while objective facts provide the necessary foundational context, relying solely on them is insufficient; they must be augmented by explicit modeling of subjective attitudes to capture a comprehensive user profile. To address this, we introduce Mem-ABC, an Attitude-Aware Agent Memory framework grounded in the Tripartite Model of Attitudes (Affect, Behavior, and Cognition). Mem-ABC employs a two-stage offline process: first autonomously refining raw history into general memories to eliminate noise, and subsequently reorganizing them into a structured attitude-aware memory. This structure enables the agent to explicitly retrieve context aligned with user psychology. Experiments on the LaMP-QA benchmark demonstrate that Mem-ABC consistently outperforms strong baselines across multiple domains."
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<abstract>Personalized Question Answering (PQA) requires models to shift from a fact-centric to a user-centric paradigm, inferring implicit intents from historical interactions. However, standard Retrieval-Augmented Generation (RAG) struggles with narrative noise and fragmented semantics. Similarly, while Agent Memory was proposed to maintain long-term consistency, existing implementations predominantly focus on accumulating objective facts, neglecting the subjective attitudes essential for personalization. We argue that for PQA, while objective facts provide the necessary foundational context, relying solely on them is insufficient; they must be augmented by explicit modeling of subjective attitudes to capture a comprehensive user profile. To address this, we introduce Mem-ABC, an Attitude-Aware Agent Memory framework grounded in the Tripartite Model of Attitudes (Affect, Behavior, and Cognition). Mem-ABC employs a two-stage offline process: first autonomously refining raw history into general memories to eliminate noise, and subsequently reorganizing them into a structured attitude-aware memory. This structure enables the agent to explicitly retrieve context aligned with user psychology. Experiments on the LaMP-QA benchmark demonstrate that Mem-ABC consistently outperforms strong baselines across multiple domains.</abstract>
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%0 Conference Proceedings
%T Mem-ABC: Attitude-Aware Agent Memory for Personalized Question Answering
%A Shi, Yinghan
%A Fu, Kai
%A Chu, Quanyou
%A Liu, Xinyu
%A Du, Ming
%A Wang, Hongya
%A Meng, Xiaojun
%A Wei, Jiansheng
%A Xiao, Yanghua
%A Xu, Bo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F shi-etal-2026-mem
%X Personalized Question Answering (PQA) requires models to shift from a fact-centric to a user-centric paradigm, inferring implicit intents from historical interactions. However, standard Retrieval-Augmented Generation (RAG) struggles with narrative noise and fragmented semantics. Similarly, while Agent Memory was proposed to maintain long-term consistency, existing implementations predominantly focus on accumulating objective facts, neglecting the subjective attitudes essential for personalization. We argue that for PQA, while objective facts provide the necessary foundational context, relying solely on them is insufficient; they must be augmented by explicit modeling of subjective attitudes to capture a comprehensive user profile. To address this, we introduce Mem-ABC, an Attitude-Aware Agent Memory framework grounded in the Tripartite Model of Attitudes (Affect, Behavior, and Cognition). Mem-ABC employs a two-stage offline process: first autonomously refining raw history into general memories to eliminate noise, and subsequently reorganizing them into a structured attitude-aware memory. This structure enables the agent to explicitly retrieve context aligned with user psychology. Experiments on the LaMP-QA benchmark demonstrate that Mem-ABC consistently outperforms strong baselines across multiple domains.
%U https://aclanthology.org/2026.findings-emnlp.1678/
%P 35815-35835
Markdown (Informal)
[Mem-ABC: Attitude-Aware Agent Memory for Personalized Question Answering](https://aclanthology.org/2026.findings-emnlp.1678/) (Shi et al., Findings 2026)
ACL
- Yinghan Shi, Kai Fu, Quanyou Chu, Xinyu Liu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, and Bo Xu. 2026. Mem-ABC: Attitude-Aware Agent Memory for Personalized Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 35815–35835, Budapest, Hungary. Association for Computational Linguistics.