arXiv:2605.12894v2 Announce Type: replace-cross
Abstract: Large Language Model (LLM) agents are increasingly deployed in settings where they interact with diverse users, including those who are uncle...
By Harshita Chopra, Kshitish Ghate, Aylin Caliskan, Tadayoshi Kohno, Chirag Shah, Natasha Jaques
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
The paper introduces a three-tier persona vector for user simulation in evaluating LLM agents, comprising 23 dimensions across demographics, behavioral traits, and emotional states, plus a query-complexity overlay. It demonstrates that these nuanced personas generate diverse, scenario-reactive conversations, leading to significant variations in agent goal achievement and compliance across different contexts. The model’s design allows for reproducible, auditable user behavior patterns without relying on learned covariance matrices.
By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Kumar Das, Jitesh Chandra Mishra, Arun Menon, Avinash Karn, Mouli V
The paper introduces a three-tier persona vector to generate diverse, realistic user inputs for evaluating tool-augmented LLM agents. The vector includes 23 dimensions: categorical demographics, continuous behavioral traits, and continuous emotional states, plus a query-complexity overlay. Experiments on 64,698 conversations show that these persona dimensions produce measurable differences in agent performance and realistic scenario-reactive behavior.
arXiv:2607. 27816v2 Announce Type: replace-cross Abstract: Role-playing agents (RPAs) have become one of the most important consumer applications of large language models.
By Yuhang Zhu, Mingxuan Du, Benfeng Xu, Jie Gao, Lingyun Yu, Hongtao Xie
PersonaCodeBench is a new benchmark that evaluates coding agents in multi‑turn, persona‑driven sessions, turning user characteristics into verifiable obligations. The benchmark’s persona pool covers roles, technical abilities, interaction preferences, and practical constraints, generating task‑grounded, evolving requirements for implementation, permissions, testing, explanation, and delivery. Evaluation uses a controller to manage user simulator responses, online requirement evaluation, and separate verifiers for native correctness and persona fulfillment, with a joint pass condition requiring adherence to budget and validity conditions.
By Haixin Wang, Jianheng Hou, Hongyu Shen, Wenzhe Fan, Wei-Chieh Huang, Qianyu Zhu, Weichen Li, Taoran Li, Yuhang Yao, Ruohan Wang, Zhejian Peng, Wanting Jiang, Yifan Chen, Shushu Liang, Haoming Li, Defu Cao, Xu Hu, Siyang Wu, Chen Yang, Haodi Hu, Haichao Zhang, Cong Wang, Zhiyuan Yu, Weizhi Zhang, Yifan Wang, Jintao Huang, Yizhou Sun, Yijun Wang, Yuexing Hao, Xiaomin Li