To what extent should we trust AI models when they extrapolate? - Roozbeh Yousefzadeh; Xuenan Cao (CoRR, 2022)

Should Machine Learning Models Report to Us When They Are Clueless? - Roozbeh Yousefzadeh; Xuenan Cao (CoRR, 2022)

Large Language Models' Understanding of Math: Source Criticism and Extrapolation. - Roozbeh Yousefzadeh; Xuenan Cao (CoRR, 2023)

Extrapolation and AI transparency: Why machine learning models should reveal when they make decisions beyond their training. - Xuenan Cao; Roozbeh Yousefzadeh (Big Data Soc., 2023)

Towards a Scalable Reference-Free Evaluation of Generative Models. - Azim Ospanov; Jingwei Zhang; Mohammad Jalali; Xuenan Cao; Andrej Bogdanov; Farzan Farnia (NeurIPS, 2024)

A Lean Dataset for International Math Olympiad: Small Steps towards Writing Math Proofs for Hard Problems. - Roozbeh Yousefzadeh; Xuenan Cao (Trans. Mach. Learn. Res., 2025)

How Large Language Models (LLMs) Extrapolate: From Guided Missiles to Guided Prompts. - Xuenan Cao (CoRR, 2025)

Advocate for Complete Benchmarks for Formal Reasoning with Formal/Informal Statements and Formal/Informal Proofs. - Roozbeh Yousefzadeh; Xuenan Cao (CoRR, 2025)