CentaurBench: Benchmarking LLM Capabilities on Augmenting vs. Automating Real-World Work Tasks
Most LLM benchmarks rank models on their ability to automate work tasks. In practice, however, models are often used to assist other (human or LLM) agents. The question that drives model selection is therefore not only which model produces the best output, but which model most improves the work of another (weaker) agent. We introduce a unified framework that evaluates the capability of models to automate and augment another agent's performance. Across seven economically grounded real-world tasks, an assistant model writes assistance text for a standardized lower-capacity worker model, which produces the deliverable. In automation mode, the assistant produces the output directly. Outputs a...
| Authors | Pattaraphon Kenny Wongchamcharoen, Kris Gulati, Min Min Fong et al. |
|---|---|
| Primary category | cs.CY |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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