Adaptive Contracts for Cost-Effective AI Delegation
When organizations delegate text generation tasks to AI providers via pay-for-performance contracts, expected payments rise when evaluation is noisy. As evaluation methods become more elaborate, the economic benefits of decreased noise are often overshadowed by increased evaluation costs. In this work, we introduce adaptive contracts for AI delegation, which allow detailed evaluation to be performed selectively after observing an initial coarse signal in order to conserve resources. We make three sets of contributions: First, we provide efficient algorithms for computing optimal adaptive contracts under natural assumptions or when core problem dimensions are small, and prove hardness of a...
| Authors | Eden Saig, Tamar Garbuz, Ariel D. Procaccia et al. |
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| Primary category | cs.GT |
| Published | 17 March 2026 |
| Last updated at source | 2 July 2026 |
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