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home / papers / 2026 / PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents

PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents

Paper · 2026 · Jizhou Tong and Sirui Zou

Summary

LLM-based role-playing agents tend to lose their characters over long conversations: traits shift, speaking style wobbles, and emotional state is ignored. PersonaForge tackles this with a psychology-based design. Each character profile has three layers: core traits (Big Five scores plus one of nine Vaillant defense mechanisms), a speaking-style matrix, and a dynamic state that tracks mood, energy and relationships. Generation follows a dual-process scheme. For emotionally or relationally critical turns, a hidden Inner Monologue is produced first and then rendered in the character's voice. Ordinary turns skip this extra step.

The authors test two claims. The first is that structured psychological dimensions constrain behaviour better than free-text descriptions. The second is that dense constraints need an intermediate cognitive workspace. On 88 annotated characters, the system improves personality consistency, sharply reduces drift over 50 turns, and also generalizes to RoleBench. Profiles can be extracted automatically from raw text, so the method works without fine-tuning. It is therefore useful as a model-agnostic guardrail for consistent character agents.

Key points

  • The dataset covers 88 characters from five literary domains, including classical Chinese literature and Western fantasy. Three experts annotated them, with overall κ = 0.78 (Big Five 0.76, DM 0.82).
  • Personality consistency rises 19.4% over a Structured-CoT baseline, and the metric correlates with human judgments at r = 0.82. Drift over 50 turns falls to 6.3%, compared with 24.8% for the baseline. Defense mechanism manifestation increases by 64.7%.
  • On RoleBench, the system reaches a 73.2% pairwise win-rate and cuts drift from 20.4% to 8.4%. It stays in character in 95.2% of RoleBreak-style adversarial attacks.
  • Selective activation of the dual process fires on roughly 40% of turns. It keeps 96% of full-system performance at 13.4% token overhead.
  • Ablations support both claims. Replacing Big Five/DM with natural language lowers PC by 0.18, and removing the dual process drops PC to 0.57, below Structured-CoT's 0.72.
  • Main runs use Gemini 2.5 Flash. An open-source DeepSeek-V3 pipeline reaches PC 0.84. Qwen2.5-7B handles only simpler defenses, not "mature" ones such as Humor.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Wuhan UniversityWuhan · China35 works · 1,127 citations🎓Southwestern University of Finance and EconomicsChengdu · China4 works · 1 citation

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Jizhou Tong and Sirui Zou. 2026. PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.386

@inproceedings{tong2026personaforge,
  title = {{PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents}},
  author = {Jizhou Tong and Sirui Zou},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.386},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.386}
}

References

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