Benchmark-based evaluation remains important for tracking frontier AI progress. But it can both overstate and understate deployed capability because it privileges tasks that can be precisely specified, automatically graded, easy to optimize for, and run with low budgets and short time horizons. We advocate for a complementary class of evaluations, which we term open-world evaluations: long-horizon, messy, real-world tasks assessed through small-sample qualitative analysis rather than benchmark-scale automation. In this paper we survey recent open-world evaluations, identify their strengths and limitations, and introduce CRUX (Collaborative Research for Updating AI eXpectations), a project for conducting such evaluations regularly. As a first instance, we task an AI agent with developing and publishing a simple iOS application to the Apple App Store. The agent completed the task with only a single avoidable manual intervention, suggesting that open-world evaluations can provide early warning of capabilities that may soon become widespread. We conclude with recommendations for designing and reporting open-world evals.
@article{arxiv.2605.20520,
title = {Open-World Evaluations for Measuring Frontier AI Capabilities},
author = {Sayash Kapoor and Peter Kirgis and Andrew Schwartz and Stephan Rabanser and J. J. Allaire and Rishi Bommasani and Harry Coppock and Magda Dubois and Gillian K Hadfield and Andrew B. Hall and Sara Hooker and Seth Lazar and Steve Newman and Dimitris Papailiopoulos and Shoshannah Tekofsky and Helen Toner and Cozmin Ududec and Arvind Narayanan},
journal= {arXiv preprint arXiv:2605.20520},
year = {2026}
}