---
title: "Who cites Tobias Nipkow — 47 papers of our library"
description: "47 papers of the Elamur Research library cite the works of Tobias Nipkow, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/tobias-nipkow/cited-by/"
---
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# Who cites Tobias Nipkow

51 citations of their works from 47 papers of our library (of the 149,439 whose bibliographies we read) · 31 with a page here, the most cited first · [← Tobias Nipkow](https://md.research.elamur.ai/free/en/authors/tobias-nipkow/)

1. [**DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models arXiv:2402.03300**Zhihong Shao, Peiyi Wang, Qihao Zhu, et al.*2024 · 2,115 citations*](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/)
2. **[Solving Quantitative Reasoning Problems with Language Models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Aitor Lewkowycz, Anders Andreassen, David Dohan, et al.*2022 · 992 citations*
3. [**Llemma: An Open Language Model For Mathematics arXiv:2310.10631**Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al.*2023 · 286 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/)
4. **[Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Janice Ahn, Rishu Verma, Renze Lou, et al.*2024 · 280 citations*
5. **[The Lean mathematical library](https://md.research.elamur.ai/free/en/papers/community-2019-lean-mathematical-library/) [ACM SIGPLAN International Conference on Certified Programs and Proofs (CPP)](https://md.research.elamur.ai/free/en/venues/acm-sigplan-international-conference-on-certified-programs-and-proofs-cpp/)**The mathlib Community*2019 · 249 citations*
6. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
7. **[HyperTree Proof Search for Neural Theorem Proving](https://md.research.elamur.ai/free/en/papers/lample-2022-hypertree-proof-search-neural-theorem/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, et al.*2022 · 151 citations*
8. **[Deep Network Guided Proof Search](https://md.research.elamur.ai/free/en/papers/loos-2017-deep-network-guided-proof-search/) [EPiC series in computing](https://md.research.elamur.ai/free/en/venues/epic-series-in-computing/)**Sarah Loos, Geoffrey Irving, Christian Szegedy, et al.*2017 · 139 citations*
9. [**Premise Selection for Theorem Proving by Deep Graph Embedding arXiv:1709.09994**Mingzhe Wang, Yihe Tang, Jian Wang, et al.*2017 · 106 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-premise-selection-theorem-proving-deep/)
10. **[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/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**George Tsoukalas, Jasper Lee, John Jennings, et al.*2024 · 93 citations*
11. [**Learning to Prove Theorems via Interacting with Proof Assistants arXiv:1905.09381**Kaiyu Yang, Jia Deng*2019 · 90 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-learning-prove-theorems-interacting-proof/)
12. [**HOList: An Environment for Machine Learning of Higher-Order Theorem Proving arXiv:1904.03241**Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, et al.*2019 · 78 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2019-holist-environment-machine-learning-higher/)
13. **[First Experiments with Neural Translation of Informal to Formal Mathematics](https://md.research.elamur.ai/free/en/papers/wang-2018-first-experiments-neural-translation-informal/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Qingxiang Wang, Cezary Kaliszyk, Josef Urban*2018 · 66 citations*
14. **[TacticToe: Learning to Prove with Tactics](https://md.research.elamur.ai/free/en/papers/gauthier-2018-tactictoe-learning-prove-tactics/) [Journal of Automated Reasoning](https://md.research.elamur.ai/free/en/venues/journal-of-automated-reasoning/)**Thibault Gauthier, Cezary Kaliszyk, Josef Urban, et al.*2018 · 57 citations*
15. **[Formalising perfectoid spaces](https://md.research.elamur.ai/free/en/papers/buzzard-2019-formalising-perfectoid-spaces/) [ACM SIGPLAN International Conference on Certified Programs and Proofs (CPP)](https://md.research.elamur.ai/free/en/venues/acm-sigplan-international-conference-on-certified-programs-and-proofs-cpp/)**Kevin Buzzard, Johan Commelin, Patrick Massot*2019 · 48 citations*
16. [**HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving arXiv:1703.00426**Cezary Kaliszyk, François Chollet, Christian Szegedy*2017 · 48 citations*](https://md.research.elamur.ai/free/en/papers/kaliszyk-2017-holstep-machine-learning-dataset-higher/)
17. [**A Survey on Deep Learning for Theorem Proving arXiv:2404.09939**Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-survey-deep-learning-theorem-proving/)
18. [**INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving arXiv:2007.02924**Yuhuai Wu, Albert Qiaochu Jiang, Jimmy Ba, et al.*2020 · 41 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-int-inequality-benchmark-evaluating-generalization/)
19. [**Learning to Reason in Large Theories without Imitation arXiv:1905.10501**Kshitij Bansal, Christian Szegedy, Markus N. Rabe, et al.*2019 · 36 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2019-learning-reason-large-theories-without/)
20. [**Process-Driven Autoformalization in Lean 4 arXiv:2406.01940**Jianqiao Lu, Yingjia Wan, Zhengying Liu, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-process-driven-autoformalization-lean-4/)
21. **[Graph Representations for Higher-Order Logic and Theorem Proving](https://md.research.elamur.ai/free/en/papers/paliwal-2019-graph-representations-higher-order-logic/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Aditya Paliwal, Sarah Loos, Markus Rabe, et al.*2019 · 33 citations*
22. [**PaMpeR: Proof Method Recommendation System for Isabelle/HOL arXiv:1806.07239**Yutaka Nagashima, Yilun He*2018 · 29 citations*](https://md.research.elamur.ai/free/en/papers/nagashima-2018-pamper-proof-method-recommendation-system/)
23. [**TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning arXiv:2102.09756**Minchao Wu, Michael Norrish, Christian Walder, et al.*2021 · 24 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-tacticzero-learning-prove-theorems-scratch/)
24. [**Autoformalizing Euclidean Geometry arXiv:2405.17216**Logan Murphy, Kaiyu Yang, Jialiang Sun, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/murphy-2024-autoformalizing-euclidean-geometry/)
25. [**LLMSTEP: LLM proofstep suggestions in Lean arXiv:2310.18457**Sean Welleck, Rahul Saha*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2023-llmstep-llm-proofstep-suggestions-lean/)
26. [**CombiBench: Benchmarking LLM Capability for Combinatorial Mathematics arXiv:2505.03171**Junqi Liu, Xiaohan Lin, Jonas Bayer, et al.*2025 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-combibench-benchmarking-llm-capability-combinatorial/)
27. **[Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency](https://md.research.elamur.ai/free/en/papers/li-2024-autoformalize-mathematical-statements-symbolic-equivalence/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zenan Li, Yifan Wu, Zhaoyu Li, et al.*2024 · 19 citations*
28. [**FormalAlign: Automated Alignment Evaluation for Autoformalization arXiv:2410.10135**Jianqiao Lu, Yingjia Wan, Yinya Huang, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-formalalign-automated-alignment-evaluation-autoformalization/)
29. [**Autoformalization in the Era of Large Language Models: A Survey arXiv:2505.23486**Ke Weng, Lun Du, Sirui Li, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/weng-2025-autoformalization-era-large-language-models/)
30. [**Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning arXiv:2502.13834**Zenan Li, Zhaoyu Li, Wen Tang, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-proving-olympiad-inequalities-synergizing-llms/)
31. [**ProofFlow: A Dependency Graph Approach to Faithful Proof Autoformalization arXiv:2510.15981**Rafael Cabral, Tuan Manh Do, Xuejun Yu, et al.*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/cabral-2025-proofflow-dependency-graph-approach-faithful/)

16 more citing papers of our library have no page here yet.

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