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Framework Tests Whether AI Agents Can Predict A/B Test Outcomes
A new arXiv paper proposes a validation framework for using AI agents to simulate the results of A/B tests, which normally require real user traffic, engineering time, and weeks of waiting. The approach conditions agents on behavioral profiles to estimate experiment outcomes before a rollout. The work focuses on how such simulations should be checked for accuracy rather than on a specific product.