arXiv:2509.26619v3 Announce Type: replace 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 d
Model saturation on existing benchmarks is an immediate and critical problem for AI development, necessitating new methods for evaluating model weaknesses.
This framework addresses the rapid obsolescence of AI benchmarks, which is crucial for continued, meaningful progress in AI capabilities by exposing genuine model limitations.
AI model evaluation can now be automatically and continuously updated with challenging benchmarks derived directly from the internet, reducing reliance on static, quickly saturated human-curated datasets.
- · AI researchers
- · Large language model developers
- · AI evaluation platforms
- · Creators of static AI benchmarks
- · Models optimized for existing, saturated benchmarks
AI models will be pushed to address more complex and nuanced challenges as internet-scale benchmarks continuously evolve.
This could accelerate the development of more robust, general-purpose AI agents by better identifying and rectifying their weaknesses.
The dynamic nature of these benchmarks may lead to a more adaptive AI development lifecycle, fundamentally changing how AI progress is measured and achieved.
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Read at arXiv cs.CL