Large language models have advanced web agents, yet current agents lack personalization capabilities. Since users rarely specify every detail of their intent, practical web agents must be able to interpret ambiguous queries by inferring user preferences and contexts. To address this challenge, we present Persona2Web, the first benchmark for evaluating personalized web agents on the real open web, built upon the clarify-to-personalize principle, which requires agents to resolve ambiguity based on user history rather than relying on explicit instructions. Persona2Web consists of: (1) user histories that reveal preferences implicitly over long time spans, (2) ambiguous queries that require agents to infer implicit user preferences, and (3) a reasoning-aware evaluation framework that enables fine-grained assessment of personalization. We conduct extensive experiments across various agent architectures, backbone models, history access schemes, and queries with varying ambiguity levels, revealing key challenges in personalized web agent behavior. For reproducibility, our codes and datasets are publicly available at https://anonymous.4open.science/r/Persona2Web-73E8.

翻译：大型语言模型推动了网络智能体的发展，但现有智能体缺乏个性化能力。由于用户很少详细说明其意图的所有细节，实用的网络智能体必须能够通过推断用户偏好和上下文来理解模糊查询。为应对这一挑战，我们提出了Persona2Web，这是首个在真实开放网络上评估个性化网络智能体的基准，其构建基于“澄清以个性化”原则，要求智能体依据用户历史而非依赖显式指令来解决模糊性。Persona2Web包含：(1) 在长时间跨度内隐式揭示用户偏好的历史记录，(2) 需要智能体推断隐式用户偏好的模糊查询，以及(3) 支持对个性化能力进行细粒度评估的推理感知评价框架。我们针对多种智能体架构、骨干模型、历史访问方案以及不同模糊程度的查询进行了广泛实验，揭示了个性化网络智能体行为中的关键挑战。为保障可复现性，我们的代码与数据集已在 https://anonymous.4open.science/r/Persona2Web-73E8 公开。