PulseAugur
EN
LIVE 23:25:55

New Ko-WideSearch benchmark reveals web agents struggle with breadth-search tasks

By PulseAugur Editorial · [3 sources] ·

A new benchmark called Ko-WideSearch has been developed to evaluate the breadth-search capabilities of web agents, focusing on exhaustive set enumeration rather than depth-based question answering. This Korean-language benchmark, constructed via an automated pipeline, comprises 228 tables across 190 entities and sixteen categories. Initial testing with twenty web agents revealed consistent failures in accurately recovering row-level attributes, even when the overall set membership was correctly identified, indicating a significant challenge for current AI systems. AI

IMPACT Highlights a critical gap in web agent capabilities, suggesting current models need improvement in structured data extraction and exhaustive enumeration.

RANK_REASON The cluster describes a new academic benchmark for evaluating AI capabilities, published on arXiv.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New Ko-WideSearch benchmark reveals web agents struggle with breadth-search tasks

How we ranked this

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Research
The cluster describes a new academic benchmark for evaluating AI capabilities, published on arXiv.
Source corroboration
3 independent sources
Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics
paper, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
105 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Minbyul Jeong ·

    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, especiall…

  2. arXiv cs.CL TIER_1 English(EN) · Minbyul Jeong ·

    Ko-WideSearch: A Korean Breadth-Search Benchmark for Exhaustive Set Enumeration by Web Agents

    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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Ko-WideSearch: A Korean Breadth-Search Benchmark for Exhaustive Set Enumeration by Web Agents

    A Korean web-agent benchmark evaluates breadth of search capabilities by requiring complete enumeration of entity memberships with attribute tables, revealing consistent failures in row recovery despite accurate set identification.

RELATED ENTITIES

RELATED TOPICS