Benchmark Difficulty vs. Real-World Work and Job Relevance

Determine whether scaling benchmark task difficulty—by increasing task complexity, extending the amount of work per task, or introducing more challenging environments—in AI agent evaluations meaningfully reflects the structure and demands of human work in the labor market, and ascertain how performance on such benchmarks translates into practical relevance for real-world jobs.

Background

The paper notes that recent agent benchmark design has largely focused on making tasks harder by increasing complexity, lengthening tasks, or using more challenging environments. However, whether such scaling meaningfully captures how real human work is structured and performed is not established.

The authors emphasize that even as difficulty increases, there is uncertainty about how well benchmark performance maps to real-world job utility, motivating their proposed framework that maps benchmarks to O*NET work domains and skills.