HuggingFace Papers
Ko-WideSearch: A Korean Breadth-Search Benchmark for Exhaustive Set Enumeration by Web Agents
evalagentic
What happened
Ko-WideSearch is a Korean-language web-agent benchmark designed to evaluate 'breadth-search' capabilities. It tests an agent's ability to exhaustively enumerate all members of a specific set and compile their attributes into tables. The benchmark reveals that while agents can often identify the correct entities, they consistently fail to accurately recover complete row/attribute data.
Why it matters
It exposes a critical failure mode in web agents when performing structured data extraction across multiple pages.
The take
Evaluating web agents on exhaustive search rather than simple QA is a step in the right direction. Most current benchmarks test single-answer retrieval, whereas real-world workflows (like market research) require exhaustive set enumeration. The failure in row recovery highlights a weak point in current agentic extraction pipelines.
Do this
If building web-scraping or research agents, implement explicit validation steps to ensure tabular data extraction matches the source page's schema before completing the run.
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