Poster Sat, Dec 12, 2026 • 8:30 AM – 11:30 AM AEDT Hall C1

JobBench: Aligning Agent Work With Human Will

Yuetai Li ⋅ Yichen Feng ⋅ Zhangchen Xu ⋅ Zixian Ma ⋅ Kaiyuan Zheng ⋅ Fengqing Jiang ⋅ Xinghua Sun ⋅ Rulin Shao ⋅ Zichen Chen ⋅ Yue Huang ⋅ Xinyang Han ⋅ Brian Lee ⋅ Kayla Xu ⋅ Shenglai Zeng ⋅ Hang Hua ⋅ Xiangliang Zhang ⋅ Basel Alomair ⋅ Ranjay Krishna ⋅ Luke Zettlemoyer ⋅ Pang Wei Koh ⋅ Bhaskar Ramasubramanian ⋅ Luyao Niu ⋅ Xiang Yue ⋅ Radha Poovendran

[ OpenReview]

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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