AGENTIC-SYSTEMSMay 22, 20261 min read9 views

Open-World Evaluations for Measuring Frontier AI Capabilities

Traditional benchmark-based AI evaluations often fail to reflect real-world capabilities. A new paper proposes "open-world evaluations" to better measure how AI agents handle complex, long-horizon, and messy real-world tasks.

Computer Science > Artificial Intelligence

Title:Open-World Evaluations for Measuring Frontier AI Capabilities

View PDF HTML (experimental)Abstract: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.

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Source: arXiv cs.AI Recent

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