Author

Sang T. Truong

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Preprint Sep 2026

What AI Benchmarks Actually Measure: Adapting Convergent and Discriminant Validity to Interrogate Fifty-Six AI Benchmarks

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.

Meera Desai, Sang T. Truong, Hanna Wallach et al. · 0 citations

#natural language process... Preprint Sep 2026

A Dataset for Modeling Iterative Problem-Solving

It is found that the model's coding proficiency is inversely related to predictive performance in this setting, with the LLM better understood as a generative solver conditioned on context rather than a faithful predictor of solver behavior.

Fagun Patel, Sang T. Truong, Duc Q. Nguyen et al. · 0 citations

Public AI Benchmarks Are Broken, But Are Private Benchmarks the Answer?

This work argues for a hybrid public-private approach: keeping the benchmark content private to maintain integrity while making the benchmark design public to build trust, which offers a path toward trustworthy measurement of ever-growing data-intensive AI systems.

Sang T. Truong, S. Wang, Angelina Wang et al. · 0 citations