@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://preview.aclanthology.org/ingest-acl/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."
}