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arXiv · 2509.26619

Searching the Internet for Challenging Benchmarks at Scale

Wenda Xu·Vilém Zouhar·Parker Riley·Mara Finkelstein·Markus Freitag·Daniel Deutsch
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Abstract

Many static benchmarks are beginning to saturate: as models rapidly improve, they achieve near-perfect scores on fixed test sets, leaving little headroom to expose genuine model weaknesses -- and even expert-curated challenge sets quickly saturate after hillclimbing. We present a fully automatic framework that searches the Internet at scale to construct challenging benchmarks without human curation. The key insight is to model the Internet as a vast space of topics and formalize the search as a multi-armed bandit problem, where each topic's difficulty is revealed only through expensive sample-and-evaluate queries. Our epsilon-greedy strategy identifies the most challenging topics while exploring only 6% of the search space -- a 100 times cost reduction over exhaustive evaluation. We validate on machine translation and knowledge question answering, confirming that discovered difficulty is robust across independent metrics (GEMBA-SQA and MetricX), languages, and models.

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Wenda Xu, Vilém Zouhar, Parker Riley, Mara Finkelstein, Markus Freitag, Daniel Deutsch. 2026-05-25. Searching the Internet for Challenging Benchmarks at Scale. https://arxiv.org/abs/2509.26619

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