JobBench: Aligning Agent Work With Human Will
Quick Take
JobBench introduces a new benchmark for AI agents, evaluating 36 models on 130 tasks across 35 occupations, with the best model, Claude Opus 4.7, achieving only 45.9%. This framework emphasizes enhancing human work rather than economic replacement, aiming to align AI outputs with actual human delegation needs.
Key Points
- JobBench evaluates AI agents based on expert-identified high-priority tasks.
- The benchmark includes 130 tasks across 35 different occupations.
- Outputs are graded using an average of 35.6 binary criteria per task.
- Claude Opus 4.7 is the top-performing model, scoring 45.9%.
- The goal is to shift focus from replacement to enhancement of human labor.
Article Excerpt
From source RSS / original summaryarXiv:2605. 26329v1 Announce Type: new Abstract: Current benchmarks for occupational AI agents are scoped primarily by economic values, telling a replacement story. We introduce JobBench, which evaluates AI agents on the workflows that experts identify as high-priority for delegation, empowering humans based on their needs instead of replacing them with GDP value. JobBench covers 130 agentic tasks across 35 occupations.
Each task is packaged as a workspace of heterogeneous reference files, requiring the agent to reason through the cluttered information streams of real professional work. Outputs are graded by a fact-anchored chain of rubrics, averaging 35. 6 binary criteria per task. We evaluate 36 models; the strongest, Claude Opus~4. 7 under Claude Code, reaches only 45. 9 %.
We hope JobBench shifts the community's target labour-market effect from replacement to enhancement: building agents that do what humans actually want delegated, not only what is most economically valuable.
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