@inproceedings{zhang-etal-2026-facts,
title = "From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents",
author = "Zhang, Rongsheng and
Hu, Ruofan and
Chen, Weijie and
Tang, Jiji and
Ren, Junnan and
Wu, Wanying and
Chen, Xunuoyan and
Lv, Tangjie and
Jin, Tao and
Zhao, Zhou",
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.270/",
pages = "5666--5686",
ISBN = "979-8-89176-462-0",
abstract = "While role-playing agents excel in short-term interactions, long-term conversations overwhelm context windows, motivating external memory frameworks. Current systems typically rely on persona-agnostic summarization, which records facts without persona-specific interpretation, yielding generic responses that compromise persona fidelity. To bridge this gap, we introduce RoleMemo, a dataset featuring four reasoning tasks where the factual fragments must be interpreted through the persona to reach the correct answer. Evaluation on RoleMemo exposes critical limitations of persona-agnostic frameworks. We thus propose DualMem, which decouples memory into two streams: factual cognition and persona-conditioned insight. Trained through Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), our framework with a 4B-parameter model outperforms zero-shot persona-agnostic frameworks powered by DeepSeek-V3.2 for sustained persona fidelity. Our resources are available at https://github.com/Collab-Gen/RoleMemo."
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<abstract>While role-playing agents excel in short-term interactions, long-term conversations overwhelm context windows, motivating external memory frameworks. Current systems typically rely on persona-agnostic summarization, which records facts without persona-specific interpretation, yielding generic responses that compromise persona fidelity. To bridge this gap, we introduce RoleMemo, a dataset featuring four reasoning tasks where the factual fragments must be interpreted through the persona to reach the correct answer. Evaluation on RoleMemo exposes critical limitations of persona-agnostic frameworks. We thus propose DualMem, which decouples memory into two streams: factual cognition and persona-conditioned insight. Trained through Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), our framework with a 4B-parameter model outperforms zero-shot persona-agnostic frameworks powered by DeepSeek-V3.2 for sustained persona fidelity. Our resources are available at https://github.com/Collab-Gen/RoleMemo.</abstract>
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%0 Conference Proceedings
%T From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents
%A Zhang, Rongsheng
%A Hu, Ruofan
%A Chen, Weijie
%A Tang, Jiji
%A Ren, Junnan
%A Wu, Wanying
%A Chen, Xunuoyan
%A Lv, Tangjie
%A Jin, Tao
%A Zhao, Zhou
%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 zhang-etal-2026-facts
%X While role-playing agents excel in short-term interactions, long-term conversations overwhelm context windows, motivating external memory frameworks. Current systems typically rely on persona-agnostic summarization, which records facts without persona-specific interpretation, yielding generic responses that compromise persona fidelity. To bridge this gap, we introduce RoleMemo, a dataset featuring four reasoning tasks where the factual fragments must be interpreted through the persona to reach the correct answer. Evaluation on RoleMemo exposes critical limitations of persona-agnostic frameworks. We thus propose DualMem, which decouples memory into two streams: factual cognition and persona-conditioned insight. Trained through Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), our framework with a 4B-parameter model outperforms zero-shot persona-agnostic frameworks powered by DeepSeek-V3.2 for sustained persona fidelity. Our resources are available at https://github.com/Collab-Gen/RoleMemo.
%U https://aclanthology.org/2026.findings-emnlp.270/
%P 5666-5686
Markdown (Informal)
[From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents](https://aclanthology.org/2026.findings-emnlp.270/) (Zhang et al., Findings 2026)
ACL
- Rongsheng Zhang, Ruofan Hu, Weijie Chen, Jiji Tang, Junnan Ren, Wanying Wu, Xunuoyan Chen, Tangjie Lv, Tao Jin, and Zhou Zhao. 2026. From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 5666–5686, Budapest, Hungary. Association for Computational Linguistics.