Anytime Contracts for AI Services

The paper introduces "Anytime Contracts" for AI services, addressing moral hazard where providers may downgrade models and users can terminate usage at any time. It extends contract theory to handle unknown exit rules by framing the problem as a Principal-Agent game and shows that optimal contracts align with anytime-valid hypothesis tests using likelihood‑ratio e‑values. The authors provide an efficient algorithm for computing such contracts and demonstrate its application on synthetic data and real large language model deployments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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