OccuBench: Can Your AI Agent Do a Real Job?
We keep saying AI agents will replace knowledge work. But how do you actually test that across hundreds of professions? OccuBench is the first systematic attempt — 100 real-world professional task scenarios across 10 industries and 65 specialized domains, from emergency department triage to customs import processing to nuclear reactor safety monitoring.
The clever trick is Language World Models. You can't build real simulators for 65 domains, so the authors use LLMs to simulate domain-specific environments through tool response generation. The multi-agent synthesis pipeline automatically produces test cases with guaranteed solvability, calibrated difficulty, and document-grounded diversity.
They threw 15 frontier models from 8 families at it. The results are sobering. No single model dominates all industries — each has a distinct occupational capability profile. GPT-5.2 shows a 27.5 point improvement from minimal to maximum reasoning effort, which means the gap between lazy and careful inference is enormous. And the hardest faults to handle aren't the obvious ones like server timeouts or 500 errors — it's implicit data degradation like truncated records and missing fields, because there's no error signal telling the agent something is wrong.
The meta insight: being good at coding benchmarks doesn't mean being good at professional tasks. Different industries stress different capabilities. This is the benchmark the enterprise AI agent market actually needed.
https://arxiv.org/abs/2604.10866
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The clever trick is Language World Models. You can't build real simulators for 65 domains, so the authors use LLMs to simulate domain-specific environments through tool response generation. The multi-agent synthesis pipeline automatically produces test cases with guaranteed solvability, calibrated difficulty, and document-grounded diversity.
They threw 15 frontier models from 8 families at it. The results are sobering. No single model dominates all industries — each has a distinct occupational capability profile. GPT-5.2 shows a 27.5 point improvement from minimal to maximum reasoning effort, which means the gap between lazy and careful inference is enormous. And the hardest faults to handle aren't the obvious ones like server timeouts or 500 errors — it's implicit data degradation like truncated records and missing fields, because there's no error signal telling the agent something is wrong.
The meta insight: being good at coding benchmarks doesn't mean being good at professional tasks. Different industries stress different capabilities. This is the benchmark the enterprise AI agent market actually needed.
https://arxiv.org/abs/2604.10866
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