@inproceedings{tong-zou-2026-personaforge,
title = "{P}ersona{F}orge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents",
author = "Tong, Jizhou and
Zou, Sirui",
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.386/",
pages = "7845--7874",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models excel at role-playing but struggle to maintain consistent personalities across extended multi-turn interactions. We propose PersonaForge, combining (1) a three-layer personality architecture grounded in psychological theory and (2) a dual-process generation mechanism inspired by cognitive science. We test two falsifiable claims: Claim 1 (Orthogonality): Psychology-grounded dimensions (Big Five + Defense Mechanisms) provide more orthogonal constraints than natural language descriptions, reducing long-dialogue drift. Claim 2 (Integration Necessity): High-dimensional personality constraints create ``production interference'' requiring a cognitive workspace (Inner Monologue) to resolve{---}removing it degrades performance below simpler baselines. Experiments on 88 characters demonstrate: (1) +19.4{\%} personality consistency (PC) with human correlation r=0.82, (2) reduced drift over 50-turn conversations (6.3{\%} vs. 24.8{\%} baseline), and (3) +64.7{\%} defense mechanism manifestation. External validation on RoleBench confirms generalization (73.2{\%} win-rate, drift 8.4{\%} vs. 20.4{\%}). Selective dual-process activation achieves 96{\%} of full-system performance with only 13.4{\%} token overhead. Human evaluation confirms more authentic and psychologically coherent character behaviors. Code and data: https://github.com/fQwQf/PersonaForge."
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<abstract>Large Language Models excel at role-playing but struggle to maintain consistent personalities across extended multi-turn interactions. We propose PersonaForge, combining (1) a three-layer personality architecture grounded in psychological theory and (2) a dual-process generation mechanism inspired by cognitive science. We test two falsifiable claims: Claim 1 (Orthogonality): Psychology-grounded dimensions (Big Five + Defense Mechanisms) provide more orthogonal constraints than natural language descriptions, reducing long-dialogue drift. Claim 2 (Integration Necessity): High-dimensional personality constraints create “production interference” requiring a cognitive workspace (Inner Monologue) to resolve—removing it degrades performance below simpler baselines. Experiments on 88 characters demonstrate: (1) +19.4% personality consistency (PC) with human correlation r=0.82, (2) reduced drift over 50-turn conversations (6.3% vs. 24.8% baseline), and (3) +64.7% defense mechanism manifestation. External validation on RoleBench confirms generalization (73.2% win-rate, drift 8.4% vs. 20.4%). Selective dual-process activation achieves 96% of full-system performance with only 13.4% token overhead. Human evaluation confirms more authentic and psychologically coherent character behaviors. Code and data: https://github.com/fQwQf/PersonaForge.</abstract>
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%0 Conference Proceedings
%T PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents
%A Tong, Jizhou
%A Zou, Sirui
%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 tong-zou-2026-personaforge
%X Large Language Models excel at role-playing but struggle to maintain consistent personalities across extended multi-turn interactions. We propose PersonaForge, combining (1) a three-layer personality architecture grounded in psychological theory and (2) a dual-process generation mechanism inspired by cognitive science. We test two falsifiable claims: Claim 1 (Orthogonality): Psychology-grounded dimensions (Big Five + Defense Mechanisms) provide more orthogonal constraints than natural language descriptions, reducing long-dialogue drift. Claim 2 (Integration Necessity): High-dimensional personality constraints create “production interference” requiring a cognitive workspace (Inner Monologue) to resolve—removing it degrades performance below simpler baselines. Experiments on 88 characters demonstrate: (1) +19.4% personality consistency (PC) with human correlation r=0.82, (2) reduced drift over 50-turn conversations (6.3% vs. 24.8% baseline), and (3) +64.7% defense mechanism manifestation. External validation on RoleBench confirms generalization (73.2% win-rate, drift 8.4% vs. 20.4%). Selective dual-process activation achieves 96% of full-system performance with only 13.4% token overhead. Human evaluation confirms more authentic and psychologically coherent character behaviors. Code and data: https://github.com/fQwQf/PersonaForge.
%U https://aclanthology.org/2026.findings-acl.386/
%P 7845-7874
Markdown (Informal)
[PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents](https://aclanthology.org/2026.findings-acl.386/) (Tong & Zou, Findings 2026)
ACL