# Adaptive Contracts for Cost-Effective AI Delegation

arXiv: https://arxiv.org/abs/2603.17212 · HTML: https://mlsanity.com/paper/2603.17212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2406.07791/) (2024)
- [Large Language Models are Inconsistent and Biased Evaluators](https://mlsanity.com/paper/2405.01724/) (2024)
- [Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems](https://mlsanity.com/paper/1405.2875/) (2014)
- [LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena](https://mlsanity.com/paper/2510.17638/) (2025)
- [Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs](https://mlsanity.com/paper/2504.04715/) (2025)
- [The Sample Complexity of Online Contract Design](https://mlsanity.com/paper/2211.05732/) (2022)
- [Simple versus Optimal Contracts](https://mlsanity.com/paper/1808.03713/) (2018)
- [Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives](https://mlsanity.com/paper/2505.21627/) (2025)
- [Binary Scoring Rules that Incentivize Precision](https://mlsanity.com/paper/2002.10669/) (2020)
- [Incentivizing Quality Text Generation via Statistical Contracts](https://mlsanity.com/paper/2406.11118/) (2024)
- [EconEvals: Benchmarks and Litmus Tests for Economic Decision-Making by LLM Agents](https://mlsanity.com/paper/2503.18825/) (2025)
- [Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services](https://mlsanity.com/paper/2505.18471/) (2025)
- [Algorithmic Contract Theory: A Survey](https://mlsanity.com/paper/2412.16384/) (2024)
- [Information Design in the Principal-Agent Problem](https://mlsanity.com/paper/2209.13688/) (2022)
- [Contract Design With Safety Inspections](https://mlsanity.com/paper/2311.02537/) (2023)
- [Contract Design Beyond Hidden-Actions](https://mlsanity.com/paper/2402.16553/) (2024)
- [Hiring for An Uncertain Task: Joint Design of Information and Contracts](https://mlsanity.com/paper/2407.05459/) (2024)

## Abstract (arXiv)

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.