AMO-Bench: Large Language Models Still Struggle in High School Math
  Competitions
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/
690426d02c556835fa67f0b0
Shengnan An
claimed_verified
2025-11-05T16:25:19.892Z
64db5f5dd68a6ddcc7bd89e9
/avatars/69375ec915927b855813df8a6d486837.svg
Shengnan An
ShengnanAn
user
ShengnanAn
690426d02c556835fa67f0b1
Xunliang Cai
690426d02c556835fa67f0b2
Xuezhi Cao
690426d02c556835fa67f0b3
Xiaoyu Li
690426d02c556835fa67f0b4
Yehao Lin
690426d02c556835fa67f0b5
Junlin Liu
claimed_verified
2025-11-03T20:54:52.506Z
6901bec989120fe64f3a941a
/avatars/47cb787eced26c86ddb7e47c63b68f4d.svg
AaronLiu
AMO-Bench
user
AMO-Bench
690426d02c556835fa67f0b6
Xinxuan Lv
690426d02c556835fa67f0b7
Dan Ma
690426d02c556835fa67f0b8
Xuanlin Wang
690426d02c556835fa67f0b9
Ziwen Wang
690426d02c556835fa67f0ba
Shuang Zhou
AMO-Bench: Large Language Models Still Struggle in High School Math
  Competitions
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/
AMO-Bench is a benchmark for evaluating advanced mathematical reasoning in large language models with problems at the International Mathematical Olympiad level or higher, revealing significant room for improvement in current models.
large language models mathematical reasoning International Mathematical Olympiad benchmark performance saturation automatic grading test-time compute