Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History
arXiv:2602.17003v2 Announce Type: replace 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 in
Large language models are rapidly advancing, creating the foundation for more sophisticated AI agents, and the focus is shifting towards practical, personalized applications for real-world web interaction.
This development signals a crucial step towards truly autonomous AI agents that can deeply understand and adapt to individual user behavior, enhancing their utility and integration into daily life.
The introduction of a benchmark for personalized web agents shifts the focus from general web interaction to context-aware, user-centric AI behavior, demanding more sophisticated reasoning from AI systems.
- · AI agent developers
- · Productivity software companies
- · E-commerce platforms
- · Users seeking personalized digital assistance
- · Generic web automation tools
- · Companies relying on static user engagement models
AI agents begin to seamlessly interpret ambiguous user requests based on inferred preferences and past interactions.
Increased reliance on personalized AI agents for complex web tasks, leading to more efficient digital workflows.
The development of highly adaptive personal AI assistants capable of anticipating needs across multiple digital platforms.
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Read at arXiv cs.CL