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A framework for evaluating vulnerability reports via probes, executable checks of security properties, which surfaced 23 previously unreported vulnerabilities, the majority of which have been confirmed by maintainers.
This work adapt convergent and discriminant validity from the social sciences into an approach for interrogating AI benchmarks, applying it to 56 capability and safety benchmarks across 53 models and asking analogous questions at the item level using item response theory (IRT) models.
This work audits ChatGPT, Claude, and Gemini across seven systems and nine benchmarks spanning general capability, social bias, and sycophancy, finding systematic API--interface differences in both accuracy and consistency.
It is shown that matched comparisons enable rigorous, calibrated memorization claims, and reveal where prior setups have validity issues, which require a valid memorization claim and near-certain generation within a realistic budget.
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