PersonaScienceBench: Evaluating Scientific Agents with Diverse Simulated Researchers

Authors: Siyang Wu , Weizhi Zhang , Chen Yang , Esther Xu Fei , Jorin Overwiening , Xu Pan , Jing Tang , Yixuan He , Kelvin C. Mo , Peiwen Li , Defu Cao , Xu Hu , Haixin Wang , Taoran Li , Haoming Li , Zheyuan Deng , Jingwen Zhang , Pengsong Zhang , Haichao Zhang , Guangze Luo , Yi Daniel Lu , Zihuai He , Chenxi Qiu , Dianzhuo Wang , Zhejian Peng , Shushu Liang , Wenyi Lu , Yifan Wang , Jintao Huang , Yijun Wang , Xiaomin Li , Yuexing Hao

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Abstract: Scientific agents collaborate with researchers whose expertise and preferences differ, but benchmarks usually hold the collaborator fixed. We introduce PersonaScienceBench: 200 tasks from five benchmarks across four scientific domains. Simulated researchers provide context and feedback according to persona profiles, while task objectives and grading remain fixed. Agents encounter researcher preferences only through conversation. Across eighteen modelconfigurations, task performance, fact recovery, and self-repetition reveal different strengths. Detailed GPT 6 Astra analyses show distinct agent responses across researcher profiles; interaction traces illustrate targeted clar ification and workflow revision. These behaviors can differ even when final scores match. PersonaScienceBench therefore evaluates how agents collaborate with diverse researchers alongside whether they complete the task.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.32106 [cs.AI]

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arXiv:2610.32106v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.32106

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arXiv-issued DOI via DataCite (pending registration)

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From: Siyang Wu [ view email ]

[v1]

Fri, 23 Oct 2026 03:11:01 UTC (237 KB)

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