From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents

πŸ“… 2026-05-25
πŸ“ˆ Citations: 0
✨ Influential: 0
πŸ“„ PDF
πŸ€– AI Summary
This work addresses the degradation of role consistency and response genericness in long-term dialogues with role-playing agents, which stems from reliance on role-agnostic memory summarization. To mitigate this, the authors propose DualMem, a novel framework featuring a dual-memory mechanism that decouples memory into two distinct channels: factual knowledge and role-driven insights, with an explicit emphasis on interpreting facts from the character’s perspective. Leveraging a newly curated RoleMemo dataset, they train a 4B-parameter model through supervised fine-tuning and reinforcement learning, further integrating an external memory architecture to better model role consistency. Experimental results demonstrate that DualMem substantially outperforms zero-shot, role-agnostic baselines built upon DeepSeek-V3.2, significantly enhancing role fidelity in extended conversations.
πŸ“ 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/role2026/rolememo.
Problem

Research questions and friction points this paper is trying to address.

role-playing agents
persona fidelity
external memory
long-term conversation
persona-agnostic summarization
Innovation

Methods, ideas, or system contributions that make the work stand out.

persona-driven memory
dual memory framework
role-playing agents
persona fidelity
long-term conversation
πŸ”Ž Similar Papers
πŸ’Ό Related Jobs
No related jobs found.
Rongsheng Zhang
Rongsheng Zhang
Fuxi AI Lab, NetEase Inc., Hangzhou, China
NLP
Ruofan Hu
Ruofan Hu
Zhe Jiang University
W
Weijie Chen
Fuxi AI Lab, Netease Inc.
J
Jiji Tang
Fuxi AI Lab, Netease Inc.
J
Junnan Ren
Fuxi AI Lab, Netease Inc.
W
Wanying Wu
Zhejiang University
X
Xunuoyan Chen
Zhejiang University
Tangjie Lv
Tangjie Lv
netease
reinforcement learning
T
Tao Jin
Zhejiang University
Zhou Zhao
Zhou Zhao
Zhejiang University
Machine LearningData MiningMultimedia Computing