Adaptive Contracts for Cost-Effective AI Delegation

[Submitted on 17 Mar 2026 (v1), last revised 2 Jul 2026 (this version, v2)]

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Abstract: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 approximation in the general unstructured case. We then formulate alternative models of randomized adaptive contracts and discuss their benefits and limitations. Finally, we empirically demonstrate the benefits of adaptivity over non-adaptive baselines using question-answering and code-generation datasets.

Submission history

From: Eden Saig [view email]
[v1] Tue, 17 Mar 2026 23:31:01 UTC (1,097 KB)
[v2] Thu, 2 Jul 2026 06:17:12 UTC (1,443 KB)