

















Abstract: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 this https URL.
| Comments: | Accepted to ICML 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2602.17003 [cs.CL] |
| (or arXiv:2602.17003v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2602.17003 arXiv-issued DOI via DataCite |
From: Serin Kim [view email]
[v1]
Thu, 19 Feb 2026 01:54:26 UTC (3,104 KB)
[v2]
Tue, 26 May 2026 17:09:16 UTC (3,121 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。