@inproceedings{cai-etal-2026-thinkpersona,
title = "{T}hink{P}ersona: Thinking with Persona Graphs for Faithful Individualized Role-Playing",
author = "Cai, Yichen and
Chen, Pei and
Li, Jiayang and
Guo, Jingya and
Li, Zejian and
Yang, Changyuan and
Sun, Lingyun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.449/",
pages = "9905--9927",
ISBN = "979-8-89176-390-6",
abstract = "Large Language Models are increasingly utilized as Role-Playing Agents (RPAs) to simulate personas in interactive settings. However, current RPAs often produce flattened and stereotypical personas with limited depth and fidelity. This limitation arises from two core challenges: insufficient modeling of complex personal histories and internal logic, and ungrounded reasoning that fails to preserve persona coherence as dialogue context evolves. To address these challenges, we propose ThinkPersona, a role-playing agent trained to explicitly ground responses in individual identity. We introduce Persona Graphs as structured representations that encode life trajectories, values, relationships, and events as interconnected knowledge. We construct 1,201 Persona Graphs from real-world interviews and derive a Question{--}Reasoning{--}Answer (QRA) dataset of 23,401 samples that supervises reasoning over persona evidence. Fine-tuning on QRA enables ThinkPersona to internalize persona logic and generate persona-consistent responses in long-context dialogues. Experiments on three benchmarks show that ThinkPersona improves role-playing fidelity, behavioral consistency, and grounded reasoning over existing methods, while preserving general instruction-following capabilities. Our code and dataset are available at https://github.com/Hualeez/ThinkPersona."
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<abstract>Large Language Models are increasingly utilized as Role-Playing Agents (RPAs) to simulate personas in interactive settings. However, current RPAs often produce flattened and stereotypical personas with limited depth and fidelity. This limitation arises from two core challenges: insufficient modeling of complex personal histories and internal logic, and ungrounded reasoning that fails to preserve persona coherence as dialogue context evolves. To address these challenges, we propose ThinkPersona, a role-playing agent trained to explicitly ground responses in individual identity. We introduce Persona Graphs as structured representations that encode life trajectories, values, relationships, and events as interconnected knowledge. We construct 1,201 Persona Graphs from real-world interviews and derive a Question–Reasoning–Answer (QRA) dataset of 23,401 samples that supervises reasoning over persona evidence. Fine-tuning on QRA enables ThinkPersona to internalize persona logic and generate persona-consistent responses in long-context dialogues. Experiments on three benchmarks show that ThinkPersona improves role-playing fidelity, behavioral consistency, and grounded reasoning over existing methods, while preserving general instruction-following capabilities. Our code and dataset are available at https://github.com/Hualeez/ThinkPersona.</abstract>
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%0 Conference Proceedings
%T ThinkPersona: Thinking with Persona Graphs for Faithful Individualized Role-Playing
%A Cai, Yichen
%A Chen, Pei
%A Li, Jiayang
%A Guo, Jingya
%A Li, Zejian
%A Yang, Changyuan
%A Sun, Lingyun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cai-etal-2026-thinkpersona
%X Large Language Models are increasingly utilized as Role-Playing Agents (RPAs) to simulate personas in interactive settings. However, current RPAs often produce flattened and stereotypical personas with limited depth and fidelity. This limitation arises from two core challenges: insufficient modeling of complex personal histories and internal logic, and ungrounded reasoning that fails to preserve persona coherence as dialogue context evolves. To address these challenges, we propose ThinkPersona, a role-playing agent trained to explicitly ground responses in individual identity. We introduce Persona Graphs as structured representations that encode life trajectories, values, relationships, and events as interconnected knowledge. We construct 1,201 Persona Graphs from real-world interviews and derive a Question–Reasoning–Answer (QRA) dataset of 23,401 samples that supervises reasoning over persona evidence. Fine-tuning on QRA enables ThinkPersona to internalize persona logic and generate persona-consistent responses in long-context dialogues. Experiments on three benchmarks show that ThinkPersona improves role-playing fidelity, behavioral consistency, and grounded reasoning over existing methods, while preserving general instruction-following capabilities. Our code and dataset are available at https://github.com/Hualeez/ThinkPersona.
%U https://aclanthology.org/2026.acl-long.449/
%P 9905-9927
Markdown (Informal)
[ThinkPersona: Thinking with Persona Graphs for Faithful Individualized Role-Playing](https://aclanthology.org/2026.acl-long.449/) (Cai et al., ACL 2026)
ACL
- Yichen Cai, Pei Chen, Jiayang Li, Jingya Guo, Zejian Li, Changyuan Yang, and Lingyun Sun. 2026. ThinkPersona: Thinking with Persona Graphs for Faithful Individualized Role-Playing. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9905–9927, San Diego, California, United States. Association for Computational Linguistics.