PersonaCodeBench: Evaluating Coding Agents in Persona-driven Human-Interactive Sessions

Authors: 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

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Abstract: Coding agents must deliver correct software while respecting users' expertise, preferences, and working conditions. Existing benchmarks increasingly incorporate interaction, yet personalized requirement fulfillment remains underexplored. We introduce PersonaCodeBench, which turns user characteristics into verifiable obligations in persona-driven multi-turn coding sessions. Our persona pool spans roles, technical abilities, interaction preferences, and practical constraints, yielding task-grounded evolved requirements for implementation, permissions, testing, explanation, and delivery. At runtime, a controller governs user simulator responses and requirement disclosure, using online requirement evaluation to provide feedback and advance the interaction. Requirements emerge progressively, with their content, disclosure conditions, and acceptance criteria fixed before evaluation. A completion verifier and a personalization verifier separately assess native correctness and persona fulfillment. Joint pass requires both adherence to the declared budget and compliance with validity conditions. We evaluate all major frontier agents, establish a comprehensive leaderboard, and conduct detailed experimental analyses to validate effectiveness. PersonaCodeBench supports reproducible evaluation of coding agents' ability to meet explicit, personalized user expectations alongside coding objectives.

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40 pages, 9 figures

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2611.05739 [cs.AI]

(or

arXiv:2611.05739v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05739

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arXiv-issued DOI via DataCite (pending registration)

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From: Haixin Wang [ view email ]

[v1]

Tue, 3 Nov 2026 23:27:20 UTC (3,500 KB)

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