Ko-WideSearch: A Korean Breadth-Search Benchmark for Exhaustive Set Enumeration by Web Agents
arXiv:2606.27595v1 Announce Type: new Abstract: Web-agent benchmarks overwhelmingly measure depth -- pinning one obscure answer behind a chain of constraints -- while breadth, exhaustively enumerating a closed set and filling each item's attributes, is barely evaluated, especially outside English. Breadth is also hard to build: certifying that a gold set is complete and every cell correct is far costlier than checking a single answer. I introduce \textsc{Ko-WideSearch}, a Korean breadth-search benchmark built by an automated synthesize-and-verify pipeline. Each task names a set-parent entity -
The proliferation of AI agents necessitates more robust and varied benchmarking, especially beyond English-centric datasets, to ensure their capabilities are genuinely generalizable and exhaustive.
This development addresses a critical gap in AI agent evaluation, moving beyond simple depth-search tasks to the more complex and economically relevant breadth-search enumeration, which is essential for real-world agent performance.
The introduction of Ko-WideSearch shifts the focus of AI agent benchmarking to include comprehensive, verifiable enumeration tasks in non-English languages, providing a more rigorous test of agent intelligence and completeness.
- · Korean tech companies
- · AI agent developers
- · NLP researchers
- · South Korea (as an AI hub)
- · AI models without strong generalization capabilities
- · English-only AI agent development approaches
AI agents will become more adept at exhaustively cataloging and attributing information in diverse linguistic contexts.
Improved breadth-search capabilities will accelerate the deployment of autonomous agents in knowledge-intensive industries requiring comprehensive data aggregation.
The development of similar benchmarks for other languages could lead to a more fragmented but ultimately more capable global AI agent ecosystem, challenging the dominance of English-centric data.
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Read at arXiv cs.CL