FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning
  Models on Automatically Verifiable Textual and Visual Questions
Authors: name: Bowen Qin; name: Chen Yue; name: Fang Yin; name: Hui Wang; name: JG Yao; name: Jiakang Liu; name: Jing-Shu Zheng; name: Miguel Hu Chen; name: Richeng Xuan; name: Shibei Meng; name: Shiqi Zhou; name: Teng Dai; name: Tong-Shuai Ren; name: Wei Cui; name: Xi Yang; name: Xialin Du; name: Xiaojing Xu; name: Xue Sun; name: Xuejing Li; name: Yaming Liu; name: Yesheng Liu; name: Ying Liu; name: Yonghua Lin; name: Yu Zhao; name: Yunduo Zhang; name: Yuwen Luo; name: Zheqi He; name: Zhiyuan He; name: Zhongyuan Wang
Published: 2025-09-21 17:53:30

Abstract: We conduct a moderate-scale contamination-free (to some extent) evaluation of
current large reasoning models (LRMs) with some preliminary findings. We also
release ROME, our evaluation benchmark for vision language models intended to
test reasoning from visual clues. We attach links to the benchmark, evaluation
data, and other updates on this website:
https://flageval-baai.github.io/LRM-Eval/