StatEval: A Comprehensive Benchmark for Large Language Models in
  Statistics
Authors: name: Yuchen Lu, name: Run Yang, name: Yichen Zhang, name: Shuguang Yu, name: Runpeng Dai, name: Ziwei Wang, name: Jiayi Xiang, name: Wenxin E, name: Siran Gao, name: Xinyao Ruan, name: Yirui Huang, name: Chenjing Xi, name: Haibo Hu, name: Yueming Fu, name: Qinglan Yu, name: Xiaobing Wei, name: Jiani Gu, name: Rui Sun, name: Jiaxuan Jia, name: Fan Zhou
Organization: Shanghai University of Finance and Economics

Large language models (LLMs) have demonstrated remarkable advances in
mathematical and logical reasoning, yet statistics, as a distinct and
integrative discipline, remains underexplored in benchmarking efforts. To
address this gap, we introduce StatEval, the first comprehensive
benchmark dedicated to statistics, spanning both breadth and depth across
difficulty levels. StatEval consists of 13,817 foundational problems covering
undergraduate and graduate curricula, together with 2374 research-level proof
tasks extracted from leading journals. To construct the benchmark, we design a
scalable multi-agent pipeline with human-in-the-loop validation that automates
large-scale problem extraction, rewriting, and quality control, while ensuring
academic rigor. We further propose a robust evaluation framework tailored to
both computational and proof-based tasks, enabling fine-grained assessment of
reasoning ability. Experimental results reveal that while closed-source models
such as GPT5-mini achieve below 57\% on research-level problems, with
open-source models performing significantly lower. These findings highlight the
unique challenges of statistical reasoning and the limitations of current LLMs.
We expect StatEval to serve as a rigorous benchmark for advancing statistical
intelligence in large language models. All data and code are available on our
web platform: https://stateval.github.io/.