arXiv AI
Sep 1

ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions

arXiv:2608.30517v1 Announce Type: new Abstract: Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce \textsc{ScienceArena}, an...

By Guangxiang Zhao, Qilong Shi, Xusen Xiao, Wenpu Liu, Yaoming Li, Linfeng Hao, Shuyang Hou, Zijian Guo, Xinrui Zhang, Yuntian Zhao, Zhengyang Wang, Wenrui Liu, Yuhan Wu, Tong Yang, Lin Sun, Xiangzheng Zhang
arXiv AI
Oct 9

An Investigation of Robustness of LLMs in Mathematical Reasoning: Benchmarking with Mathematically-Equivalent Transformation of Advanced Mathematical Problems

The paper introduces GAP, a methodology that automatically generates mathematically equivalent variants of existing math problems using surface renames and kernel rewrites. Applying GAP to all 1,051 William Lowell Putnam Competition problems creates PutnamGAP, a 6,306-item dataset with 5,255 unseen variants. Evaluation of 18 commercial and open-source LLMs shows accuracy drops across all models, especially under kernel rewrites, indicating a persistent weakness in transferring proof plans to new mathematical settings.

By Yuren Hao, Xiang Wan, ChengXiang Zhai