Persona2Web: 基于用户历史进行上下文推理的个性化Web智能体基准
Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History
AI总结：
提出Persona2Web基准，通过澄清-个性化原则评估Web智能体在真实开放网络中利用用户历史解决模糊查询的个性化能力，并引入推理感知评估框架。
摘要：
大型语言模型推动了Web智能体的发展，但当前的智能体缺乏个性化能力。由于用户很少明确说明其意图的每个细节，实用的Web智能体必须能够通过推断用户偏好和上下文来解释模糊查询。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Web智能体的基准，基于澄清-个性化原则构建，要求智能体根据用户历史而非依赖显式指令来解决歧义。Persona2Web包括：(1) 在长时间跨度内隐含揭示偏好的用户历史，(2) 需要智能体推断隐含用户偏好的模糊查询，以及(3) 一个推理感知评估框架，能够对个性化进行细粒度评估。我们针对各种智能体架构、骨干模型、历史访问方案和不同模糊程度的查询进行了广泛实验，揭示了个性化Web智能体行为中的关键挑战。为便于复现，我们的代码和数据集公开在 https://serin-kimm.github.io/Persona2Web/。
英文摘要：
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://serin-kimm.github.io/Persona2Web/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
