--- title: "Who cites PutnamBench: Evaluating Neural — 87 citing papers" description: "87 papers of the Elamur Research library cite «PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical…" canonical: "https://research.elamur.ai/free/en/papers/tsoukalas-2024-putnambench-evaluating-neural-theorem-provers/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/tsoukalas-2024-putnambench-evaluating-neural-theorem-provers/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [PutnamBench: Evaluating Neural Theorem-Provers on…](https://md.research.elamur.ai/free/en/papers/tsoukalas-2024-putnambench-evaluating-neural-theorem-provers/) / cited by # Who cites «PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition» 87 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition»](https://md.research.elamur.ai/free/en/papers/tsoukalas-2024-putnambench-evaluating-neural-theorem-provers/) 1. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations* 2. [**DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence arXiv:2606.19348**DeepSeek-AI, Anyi Xu, Bangcai Lin, et al.*2026 · 122 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2026-deepseek-v4-highly-efficient/) 3. [**DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition arXiv:2504.21801**Z. Z. Ren, Zhihong Shao, Junxiao Song, et al.*2025 · 122 citations*](https://md.research.elamur.ai/free/en/papers/ren-2025-deepseek-prover-v2-advancing-formal/) 4. **[MathArena: Evaluating LLMs on Uncontaminated Math Competitions](https://md.research.elamur.ai/free/en/papers/balunovic-2025-matharena-evaluating-llms-uncontaminated-math/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Mislav Balunović, Jasper Dekoninck, Ivo Petrov, et al.*2025 · 99 citations* 5. [**FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI arXiv:2411.04872**Elliot Glazer, Ege Erdil, Tamay Besiroglu, et al.*2024 · 97 citations*](https://md.research.elamur.ai/free/en/papers/glazer-2024-frontiermath-benchmark-evaluating-advanced-mathematical/) 6. [**Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning arXiv:2504.11354**Haiming Wang, Mert Unsal, Xiaohan Lin, et al.*2025 · 92 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-kimina-prover-preview-large-formal/) 7. [**Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving arXiv:2502.07640**Yong Lin, Shange Tang, Bohan Lyu, et al.*2025 · 84 citations*](https://md.research.elamur.ai/free/en/papers/lin-2025-goedel-prover-frontier-model-open/) 8. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/) 9. [**Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction arXiv:2508.03613**Yong Lin, Shange Tang, Bohan Lyu, et al.*2025 · 67 citations*](https://md.research.elamur.ai/free/en/papers/lin-2025-goedel-prover-v2-scaling-formal/) 10. **[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* 11. **[ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection](https://md.research.elamur.ai/free/en/papers/yan-2026-errorradar-benchmarking-complex-mathematical-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yibo Yan, Shen Wang, Jiahao Huo, et al.*2026 · 23 citations* 12. [**Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience arXiv:2512.17260**Jiangjie Chen, Wenxiang Chen, Jiacheng Du, et al.*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-seed-prover-1-5/) 13. **[ATLAS: Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of Data](https://md.research.elamur.ai/free/en/papers/liu-2025-atlas-autoformalizing-theorems-through-lifting/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiaoyang Liu, Kangjie Bao, Jiashuo Zhang, et al.*2025 · 16 citations* 14. [**The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs arXiv:2506.21621**Jasper Dekoninck, Ivo Petrov, Kristian Minchev, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/dekoninck-2025-open-proof-corpus-large-scale/) 15. [**ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization arXiv:2510.24592**Guoxin Chen, Jing Wu, Xinjie Chen, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-reform-reflective-autoformalization-prospective-bounded/) 16. [**StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion arXiv:2508.04440**Yutong Wu, Di Huang, Ruosi Wan, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wu-2025-stepfun-formalizer-unlocking-autoformalization/) 17. **[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* 18. **[FormalScience: Scalable Human-in-the-Loop Autoformalisation of Science with Agentic Code Generation in Lean](https://md.research.elamur.ai/free/en/papers/meadows-2026-formalscience-scalable-human-loop-autoformalisation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jordan Meadows, Lan Zhang, Andre Freitas*2026* 19. **[PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models](https://md.research.elamur.ai/free/en/papers/ouyang-2026-pyramathbench-evaluating-improving-mathematical-capability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zetian Ouyang, Linlin Wang, Gerard de Melo, et al.*2026* 68 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/tsoukalas-2024-putnambench-evaluating-neural-theorem-provers/) — PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition