Haoxuan Chen
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2022
Bio
Personal website: https://haoxuanstevec00.github.io/
Education & Certifications
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Bachelor of Science, California Institute of Technology, Mathematics & Information and Data Sciences (2022)
All Publications
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A benchmark of expert-level academic questions to assess AI capabilities.
Nature
2026; 649 (8099): 1139-1146
Abstract
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve more than 90% accuracy on popular benchmarks such as Measuring Massive Multitask Language Understanding1, limiting informed measurement of state-of-the-art LLM capabilities. Here, in response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be an expert-level closed-ended academic benchmark with broad subject coverage. HLE consists of 2,500 questions across dozens of subjects, including mathematics, humanities and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable but cannot be quickly answered by internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a marked gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai .
View details for DOI 10.1038/s41586-025-09962-4
View details for PubMedID 41606155
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When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality
edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S.
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023
View details for Web of Science ID 001230083405020