AI Benchmarks Are Saturating, Threatening Real-World Progress Signals
A study of 60 language-model benchmarks finds almost half have hit saturation, with older tests worsening the effect. Expert-curated benchmarks resist this decay, while public test sets do not. The result warns that headline scores may no longer reflect real-world capability, urging new evaluation designs.