[2509.25106] Towards Personalized Deep Research: Benchmarks and Evaluations

Computer Science > Computation and Language arXiv:2509.25106 (cs) [Submitted on 29 Sep 2025 (v1), last revised 4 Mar 2026 (this version, v3)] Title:Towards Personalized Deep Research: Benchmarks and Evaluations Authors: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 View a PDF of the paper titled Towards Personalized Deep Research: Benchmarks and Evaluations, by Yuan Liang and 13 other authors View PDF HTML (experimental) 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 handl...