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JobBench is a benchmark built from worker surveys to evaluate AI agents on tasks that workers most want automated, covering 130 tasks across 35 professions with detailed rubrics.
This paper proposes a three-step framework for designing and reporting benchmarks for knowledge work AI, emphasizing alignment between benchmark tasks and real-world work activities. It derives 18 work activities from the O*NET database and analyzes three existing benchmarks (GDPval, OfficeQA Pro, APEX-SWE) to demonstrate gaps between benchmark scores and actual work capability.