Towards Personalized Deep Research: Benchmarks and Evaluations

Yuan Liang; Jiaxian Li; Yuqing Wang; Piaohong Wang; Motong Tian; Pai Liu; Shuofei Qiao; Runnan Fang; He Zhu; Ge Zhang; Minghao Liu; Yuchen Eleanor Jiang; Ningyu Zhang; Wangchunshu Zhou

arXiv · 2509.25106

Towards Personalized Deep Research: Benchmarks and Evaluations

Yuan Liang·Jiaxian Li·Yuqing Wang·Piaohong Wang·Motong Tian·Pai Liu·Shuofei Qiao·Runnan Fang·He Zhu·Ge Zhang·Minghao Liu·Yuchen Eleanor Jiang·Ningyu Zhang·Wangchunshu Zhou

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Abstract

Deep Research Agents (DRAs) can autonomously conduct complex investigations and generate comprehensive reports, demonstrating strong real-world potential. However, existing evaluations mostly rely on close-ended benchmarks, while open-ended deep research benchmarks remain scarce and typically neglect personalized scenarios. To bridge this gap, we introduce Personalized Deep Research Bench (PDR-Bench), the first benchmark for evaluating personalization in DRAs. It pairs 50 diverse research tasks across 10 domains with 25 authentic user profiles that combine structured persona attributes with dynamic real-world contexts, yielding 250 realistic user-task queries. To assess system performance, we propose the PQR Evaluation Framework, which jointly measures Personalization Alignment, Content Quality, and Factual Reliability. Our experiments on a range of systems highlight current capabilities and limitations in handling personalized deep research. This work establishes a rigorous foundation for developing and evaluating the next generation of truly personalized AI research assistants.

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Yuan Liang, Jiaxian Li, Yuqing Wang, Piaohong Wang, Motong Tian, Pai Liu, Shuofei Qiao, Runnan Fang, He Zhu, Ge Zhang, Minghao Liu, Yuchen Eleanor Jiang, Ningyu Zhang, Wangchunshu Zhou. 2026-03-04. Towards Personalized Deep Research: Benchmarks and Evaluations. https://arxiv.org/abs/2509.25106

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