PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails
Quick Answer
PersonaTrail introduces a benchmark for personalized web agents, enabling them to infer user preferences from browsing histories.
Quick Take
The Preference-Aware Contextual Memory (PACMem) framework outperforms existing memory-based models, enhancing agents' navigation capabilities by utilizing structured factual and preference memories.
Key Points
- PersonaTrail benchmarks agents in a managed open web environment using realistic browsing trajectories.
- PACMem decomposes browsing histories into factual and preference memories for better user context understanding.
- Extensive experiments show PACMem consistently outperforms existing memory-based baselines.
- The benchmark addresses the gap in existing evaluations that overlook personalized user interactions.
Paper Resources
Source Excerpt
Recent advances in have enabled web agents to autonomously execute complex tasks. In practice, users frequently provide underspecified instructions, requiring agents to infer the missing context from their raw browsing histories. Existing benchmarks fail to capture this form of personalization, as they either restrict tasks to fully explicit prompts or abstract web interaction history into simplified forms. To bridge this gap, we introduce PersonaTrail, a benchmark for pers
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