arXiv:2607.00007v1 Announce Type: cross Abstract: Large language model (LLM)-based web agents reduce manual scripting for web data collection, yet on live websites, they often miss relevant pages, return incomplete multimodal outputs, or return media URLs that are not directly downloadable. We present BFS-and-Reflection Agent (BaRA), a framework for site-level collection under a fixed interaction budget. The framework combines bounded breadth-first search (BFS) traversal with history-based self-reflection. We evaluate BaRA on 50 synthetic websites with ground-truth reference sets. We additiona
The proliferation of LLMs and the increasing demand for efficient web data collection are driving innovation in AI agent capabilities.
This development represents progress in automating complex online tasks, potentially redefining how businesses gather intelligence and interact with the digital world.
The ability of AI agents to autonomously and comprehensively collect web data will improve, reducing reliance on manual scripting and enhancing data quality.
- · AI Agent developers
- · Data intelligence firms
- · Businesses requiring web data
- · Organizations with complex online operations
- · Manual web scraping services
- · Companies with inefficient data collection methods
Companies will gain access to more complete and accurate web data with less human intervention.
The improved data collection capabilities could lead to more sophisticated competitive intelligence, market analysis, and automated business processes.
Enhanced agentic web data collection might accelerate the development of fully autonomous digital operations and digital twin applications for businesses.
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Read at arXiv cs.AI