--- title: "Who cites MathOdyssey: Benchmarking — 32 citing papers" description: "32 papers of the Elamur Research library cite «MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models…" canonical: "https://research.elamur.ai/free/en/papers/fang-2024-mathodyssey-benchmarking-mathematical-problem-solving/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/fang-2024-mathodyssey-benchmarking-mathematical-problem-solving/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [MathOdyssey: Benchmarking Mathematical…](https://md.research.elamur.ai/free/en/papers/fang-2024-mathodyssey-benchmarking-mathematical-problem-solving/) / cited by # Who cites «MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data» 32 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data»](https://md.research.elamur.ai/free/en/papers/fang-2024-mathodyssey-benchmarking-mathematical-problem-solving/) 1. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/) 2. [**Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models arXiv:2410.07985**Bofei Gao, Feifan Song, Zhe Yang, et al.*2024 · 163 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-omni-math-universal-olympiad-level/) 3. [**DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning arXiv:2504.11456**Zhiwei He, Tian Liang, Jiahao Xu, et al.*2025 · 78 citations*](https://md.research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/) 4. [**LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning arXiv:2410.02884**Di Zhang, Jianbo Wu, Jingdi Lei, et al.*2024 · 49 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-llama-berry-pairwise-optimization-o1/) 5. [**InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models arXiv:2503.06692**Yuchen Yan, Yongliang Shen, Yang Liu, et al.*2025 · 29 citations*](https://md.research.elamur.ai/free/en/papers/yan-2025-inftythink-breaking-length-limits-long/) 6. **[Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://md.research.elamur.ai/free/en/papers/balepur-2025-which-these-best-describes-multiple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber*2025 · 28 citations* 7. **[Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2026-challenging-boundaries-reasoning-olympiad-level/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxiang Sun, Yingqian Min, Zhipeng Chen, et al.*2026 · 25 citations* 8. [**U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs arXiv:2412.03205**Konstantin Chernyshev, Vitaliy Polshkov, Ekaterina Artemova, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/chernyshev-2024-u-math-university-level-benchmark/) 9. [**Brains vs. Bytes: Evaluating LLM Proficiency in Olympiad Mathematics arXiv:2504.01995**Hamed Mahdavi, Alireza Hashemi, Majid Daliri, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/mahdavi-2025-brains-vs-bytes-evaluating-llm/) 10. [**A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics arXiv:2502.14333**Ting-Ruen Wei, Haowei Liu, Xuyang Wu, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wei-2025-survey-feedback-based-multi-step/) 11. **[Graders Should Cheat: Privileged Information Enables Expert-Level Automated Evaluations](https://md.research.elamur.ai/free/en/papers/zhou-2025-graders-should-cheat-privileged-information/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jin Peng Zhou, Séb Arnold, Nan Ding, et al.*2025 · 3 citations* 12. **[A Survey of Deep Learning for Geometry Problem Solving](https://md.research.elamur.ai/free/en/papers/ma-2026-survey-deep-learning-geometry-problem/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianzhe Ma, Wenxuan Wang, Qin Jin*2026 · 1 citation* 13. **[MATH-IDN: A Multilingual Mathematical Problem Solving Dataset Featuring Local Languages in Indonesia](https://md.research.elamur.ai/free/en/papers/xiao-2026-math-idn-multilingual-mathematical-problem/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiao Xiao, Iftitahu Ni'mah, Yuyun Wabula, et al.*2026* 19 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/fang-2024-mathodyssey-benchmarking-mathematical-problem-solving/) — MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data