Personality recognition has traditionally been constrained by theory-dependent formulations, where models are trained to fit predefined psychological taxonomies rather than uncovering shared underlying behavioral structure. This limits generalization, as personality itself is better understood as theory-invariant, while existing annotations reflect only partial and sometimes inconsistent views of the same latent traits.
arXiv:2605.29791v2 Announce Type: replace
Abstract: While Large Language Models (LLMs) can convincingly simulate personas in explicit self-reports, they often deviate in implicit behavioral decisions...
By Yutong Yang, Chenxi Miao, Weikang Li, Yunfang Wu
arXiv:2606.11269v2 Announce Type: replace
Abstract: Personality assessment aims to infer stable traits from dynamic behaviors across modalities like language, voice, and facial expressions. Existing...
By Jia Li, Qian Chen, Wei Wang, Xinyu Li, Zhenzhen Hu, Dongsheng Shao, Richang Hong, Meng Wang
arXiv:2607. 26853v1 Announce Type: cross Abstract: Human personality theories characterize traits not as isolated attributes captured by a single score, but as stable individual tendencies expressed through the interplay among persons, situations, and behaviors.
By Ruikang Zhang, Shuo Wang, Qi Su
Traits Run Deeper introduces a personality assessment framework that tailors multimodal fusion to each trait dimension. It comprises a Multimodal Foundation Representation module that uses psychology-informed semantic templates, a Trait-Specific Modality Fusion module that asymmetrically fuses modalities to reduce cross‑modal interference, and a Distribution‑Calibrated Personality Regression module that corrects label imbalance. The approach achieves a ~25% reduction in mean squared error on the AVI Challenge 2026 validation set and wins the Personality Assessment Track.
By Jia Li, Qian Chen, Wei Wang, Xinyu Li, Zhenzhen Hu, Dongsheng Shao, Richang Hong, Meng Wang
arXiv:2604. 24079v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue.
By Jisoo Yang, Jongwon Ryu, Minuk Ma, Trung X. Pham, Junyeong Kim
PACIFIC is a framework that aligns large language model responses with user preferences by leveraging stable Big‑Five personality traits as a latent signal. The authors built a 1,200‑pair dataset covering diverse domains and trait directions, and found that trait‑aligned contexts enable LLMs to achieve near‑ceiling accuracy (up to 99%) in personalized QA. They also introduced a persona‑aware contrastive retriever (PiRAG) that improves label‑free accuracy from 30% to 43% over standard semantic retrieval, highlighting retrieval as the main bottleneck.
By Tianyu Zhao, Siqi Li, Yasser Shoukry, Salma Elmalaki
arXiv:2607. 07916v1 Announce Type: new Abstract: Large language models exhibit recurring behavioural patterns -- personas -- that shape generalisation and safety, but we lack reliable tools for decomposing, measuring, and controlling them.
By Luke Baines, Anton Gonzalvez Hawthorne, Mariia Koroliuk, Irakli Shalibashvili, Cl\'ement Dumas, Konstantinos Voudouris, David Demitri Africa
arXiv:2606. 18263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human populations via persona prompting, often under the assumptions that richer persona descriptions improve behavioral fidelity, similarly sized attribute combinations are equally simulatable, and persona definitions generalize across tasks.
By Aanisha Bhattacharyya, Yaman Kumar Singla, Rajiv Ratn Shah, Changyou Chen, Jitendra Ajmera
arXiv:2606. 29900v1 Announce Type: cross Abstract: Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment.
By Tianyi Zhang, Wei Shan, Yuan Zong, Tianhua Qi, Wenming Zheng
Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment. Existing approaches often rely on large language models (LLMs) to analyze textual responses of interviewees in AVI.
arXiv:2606. 28770v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated the ability to simulate human-like OCEAN personality traits in generated text.
By David Courtis, Ting Hu