arXiv AI

Data-Driven Persona-Conditioned Agents for A/B Test Simulation

The paper introduces a simulation framework that uses large language model (LLM) agents conditioned on data‑driven personas to predict A/B test outcomes. These personas are built from anonymized user behavioral patterns, engagement signals, and inferred demographics, offering a more realistic population model than synthetic or rule‑based personas. The authors evaluate question design, persona data source, behavioral depth versus diversity, and population subsampling, achieving 0.75–0.90 directional accuracy on 40 real A/B tests.

arXiv AI

Aug 6

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

arXiv:2608. 04205v1 Announce Type: new Abstract: Human evaluation of AI systems and digital products is costly, slow, and difficult to scale.

By Xiaomin Li, Yuexing Hao, Jianheng Hou, Jintao Huang, Qianfeng Wen, Shirley Huang, Yifan Liu, Xiaoyi Liu, Yilan Fan, Yijun Wang, Koutian Wu, Ruoqi Gao, Muhammad Ahmed Mohsin, Jing Tang, Brihi Joshi, Heming Liu, Zheyuan Deng, Zonglin Di, Sankalp Jajee, Jiuyao Lu, Zhiwei Zhang, Saksham Kapoor, Ishan Gupta, Yunhan Zhao, Chanwoo Park, Yucheng Lu, Bing Hu, Weihang Xiao, Aravind Mohan, Hanwen Xing, Runyu Zhang, Mihir Kulshreshtha, Yuanda Xu, Qianyu Zhu, Dianzhuo Wang, Yuxin Xiao, Bowen Jiang, Yongye Su, Wenhao Chai, Zuxin Liu, Lawrence Yunliang Chen, Xuandong Zhao, Ethan Ye, Shivam Patel, Jason Xie, Alex Martin Richmond, Weixiang Ding, Emre Okcular, Diya Mathew, Ziheng Wang, Rana M. Shahroz Khan, Zhejian Peng, Fang Wu, Fan Nie, Xinyang Han, Yubin Kim, Jiawei Zhang, Zhenting Qi, Huangyuan Su, Xu Pan, Abinitha Gourabathina, Hyewon Jeong, Hemanth Neelgund Ramesh, Kumail Alhamoud, Kimia Hamidieh, Zidi Xiong, Samuel Schmidgall, Pengrui Han, Yepeng Huang, Yongheng Wang, Bowen Yang, Alex Gu, Yuchu Wang, Akshay Paruchuri, Brenna Li, Hejie Cui, Jiayuan Ding, Chaosheng Dong, Jiahao Wang, Yixuan He, Chi Wang, Pamela Bhattacharya, Tianyi Peng, Paul Pu Liang, Mitchell Gordon, Yilun Du, Marinka Zitnik, James Zou, Prasanna Tambe, Philip Torr, Emily Fox, Asu Ozdaglar, Dawn Song

llms agents

arXiv AI

Jun 18

How Well Do Large Language Models Capture Human Personality?

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

llms diffusion safety

arXiv AI

Jun 30

LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

arXiv:2411. 10109v3 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.

By Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst, Niles Egan, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Percy Liang, Robb Willer, Michael S. Bernstein

llms agents benchmarks

arXiv AI

Jul 31

When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses

arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.

By Zihan Chen, Di Zhu, Lei Nico Zheng

llms benchmarks

arXiv AI

2d ago

Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning

arXiv:2609.00014v1 Announce Type: cross Abstract: Persona-driven techniques increasingly adapt large language models (LLMs) to diverse contexts. However, existing methods predominantly rely on rigid,...

By Yuxuan Li, Victor Zhong, Ehsan Kamalloo

llms fine-tuning benchmarks safety

arXiv Computation and Language

Aug 21

Generating Diverse Personas for User Simulators to Test Interview Dialogue Systems

arXiv:2608. 19549v1 Announce Type: new Abstract: This paper addresses the issue of the significant labor required to test interview dialogue systems.

By Mikio Nakano, Kazunori Komatani, Hironori Takeuchi

llms

arXiv Computation and Language

4d ago

PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems

PersonaForge is a user‑simulation framework that generates realistic multi‑turn interactions between users and agentic systems, addressing the gap that most training data assumes single‑turn queries. It uses a four‑dimensional persona space, SOUL‑driven behavioral control calibrated to real‑user statistics, and Reverse Deep Construction from authentic seed queries to create a 6.3K‑record training set and a 138‑task benchmark called PersonaForge‑Bench across 20 professional domains. Experiments with Qwen3.5‑27B show that training with PersonaForge improves composite scores by 4.1%, especially in Task Completion (+6.0%) and Response Quality (+6.8%), while also reducing turns and tool calls, indicating more efficient interactions.

By Hanglong Lv, Dawei Zhu, Lei Li, Bowen Ye, Huaqiu Liu, Yifan Song, Bofei Gao, Weimin Xiong, Jinhao Dong, Chenhong He, Lingpeng Kong, Qi Liu, Tong Yang, Fuli Luo

llms agents benchmarks

arXiv AI

Jun 17

Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure

arXiv:2606. 17441v1 Announce Type: cross Abstract: Simulating realistic patient interactions is a key requirement to testing clinical applications of LLMs at scale without time-consuming and expensive user studies.

By Moritz Schlager, Friederike Jungmann, Samuel Schmidgall, Philipp Raffler, Franziska Hartl, Eva Wende, Paula Ro{\ss}m\"uller, Conrad Ketzer, Avinatan Hassidim, Dale R. Webster, Yossi Matias, Yun Liu, Daniel Rueckert, Mike Schaekermann, Paul Hager

llms benchmarks

arXiv Machine Learning

Jun 30

Synthetic Interaction Data for Scalable Personalization in Large Language Models

arXiv:2602. 12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users.

By Yuchen Ma, Yue Huang, Wenjie Wang, Xiaonan Luo, Xiangliang Zhang, Stefan Feuerriegel

llms agents reinforcement-learning benchmarks safety

arXiv AI

Aug 7

LUNAR: Benchmarking Personalized Large Language Models on UNiversal User BehAvioR Logs

arXiv:2608. 05246v1 Announce Type: new Abstract: Existing personalized LLM benchmarks primarily rely on textual personas or isolated behavioral signals, providing limited evaluation of cross-domain behavioral personalization, where responses must be grounded in heterogeneous daily-life activities.

By Jiahao Zhang, Yongzhi Tong, Zelin Fu, Pengde Zhao, Yanmei Jiang, Jiang Feng, Min Yang

llms efficiency benchmarks safety

arXiv AI

3d ago

HumanStudy-Bench: Towards AI Agent Design for Participant Simulation

arXiv:2602.00685v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as simulated participants in social science experiments, but their behavior is often unstable an...

By Xuan Liu, Haoyang Shang, Zizhang Liu, Xinyan Liu, Yunze Xiao, Yiwen Tu, Haojian Jin

llms agents

arXiv AI

Aug 25

When Persona Simulations Are Informative: Graph-Structured Signals for Pluralistic Opinion Sensing

arXiv:2608.22438v1 Announce Type: new Abstract: Persona-conditioned large language models (LLMs) are increasingly used to simulate survey responses across diverse domains. However, apparent response...

By Taehyeon An, Jaehyeong Park, Donghyuk Shin

llms benchmarks

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