@inproceedings{luong-etal-2025-towards,
title = "Towards Robust Mathematical Reasoning",
author = "Luong, Thang and
Hwang, Dawsen and
Nguyen, Hoang H and
Ghiasi, Golnaz and
Chervonyi, Yuri and
Seo, Insuk and
Kim, Junsu and
Bingham, Garrett and
Lee, Jonathan and
Mishra, Swaroop and
Zhai, Alex and
Hu, Huiyi and
Michalewski, Henryk and
Kim, Jimin and
Ahn, Jeonghyun and
Bae, Junhwi and
Song, Xingyou and
Trinh, Trieu Hoang and
Le, Quoc V and
Jung, Junehyuk",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1794/",
pages = "35406--35430",
ISBN = "979-8-89176-332-6",
abstract = "Finding the right north-star metrics is highly critical for advancing mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focusing on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMOAnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-ProofBench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0{\%} on IMO-AnswerBench and 65.7{\%} on the advanced IMO-ProofBench, surpassing the best non-Gemini models by large margins of 6.9{\%} and 42.4{\%} respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://github.com/google-deepmind/superhuman/imobench."
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<abstract>Finding the right north-star metrics is highly critical for advancing mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focusing on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMOAnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-ProofBench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0% on IMO-AnswerBench and 65.7% on the advanced IMO-ProofBench, surpassing the best non-Gemini models by large margins of 6.9% and 42.4% respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://github.com/google-deepmind/superhuman/imobench.</abstract>
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%0 Conference Proceedings
%T Towards Robust Mathematical Reasoning
%A Luong, Thang
%A Hwang, Dawsen
%A Nguyen, Hoang H.
%A Ghiasi, Golnaz
%A Chervonyi, Yuri
%A Seo, Insuk
%A Kim, Junsu
%A Bingham, Garrett
%A Lee, Jonathan
%A Mishra, Swaroop
%A Zhai, Alex
%A Hu, Huiyi
%A Michalewski, Henryk
%A Kim, Jimin
%A Ahn, Jeonghyun
%A Bae, Junhwi
%A Song, Xingyou
%A Trinh, Trieu Hoang
%A Le, Quoc V.
%A Jung, Junehyuk
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F luong-etal-2025-towards
%X Finding the right north-star metrics is highly critical for advancing mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focusing on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMOAnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-ProofBench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0% on IMO-AnswerBench and 65.7% on the advanced IMO-ProofBench, surpassing the best non-Gemini models by large margins of 6.9% and 42.4% respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://github.com/google-deepmind/superhuman/imobench.
%U https://aclanthology.org/2025.emnlp-main.1794/
%P 35406-35430
Markdown (Informal)
[Towards Robust Mathematical Reasoning](https://aclanthology.org/2025.emnlp-main.1794/) (Luong et al., EMNLP 2025)
ACL
- Thang Luong, Dawsen Hwang, Hoang H Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu Hoang Trinh, Quoc V Le, and Junehyuk Jung. 2025. Towards Robust Mathematical Reasoning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 35406–35430, Suzhou, China. Association for Computational Linguistics.