AMO-Bench: Large Language Models Still Struggle in High School Math
  Competitions
Authors: name: Shengnan An, name: Xunliang Cai, name: Xuezhi Cao, name: Xiaoyu Li, name: Yehao Lin, name: Junlin Liu, name: Xinxuan Lv, name: Dan Ma, name: Xuanlin Wang, name: Ziwen Wang, name: Shuang Zhou
Organization: LongCat

We present AMO-Bench, an Advanced Mathematical reasoning benchmark with
Olympiad level or even higher difficulty, comprising 50 human-crafted problems.
Existing benchmarks have widely leveraged high school math competitions for
evaluating mathematical reasoning capabilities of large language models (LLMs).
However, many existing math competitions are becoming less effective for
assessing top-tier LLMs due to performance saturation (e.g., AIME24/25). To
address this, AMO-Bench introduces more rigorous challenges by ensuring all 50
problems are (1) cross-validated by experts to meet at least the International
Mathematical Olympiad (IMO) difficulty standards, and (2) entirely original
problems to prevent potential performance leakages from data memorization.
Moreover, each problem in AMO-Bench requires only a final answer rather than a
proof, enabling automatic and robust grading for evaluation. Experimental
results across 26 LLMs on AMO-Bench show that even the best-performing model
achieves only 52.4% accuracy on AMO-Bench, with most LLMs scoring below 40%.
Beyond these poor performances, our further analysis reveals a promising
scaling trend with increasing test-time compute on AMO-Bench. These results
highlight the significant room for improving the mathematical reasoning in
current LLMs. We release AMO-Bench to facilitate further research into
advancing the reasoning abilities of language models.
https://amo-bench.github.io/