---
title: "Who cites Niklas Een — 78 papers of our library"
description: "78 papers of the Elamur Research library cite the works of Niklas Een, found in their bibliographies; the most cited first."
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---
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# Who cites Niklas Een

81 citations of their works from 78 papers of our library (of the 149,439 whose bibliographies we read) · 45 with a page here, the most cited first · [← Niklas Een](https://md.research.elamur.ai/free/en/authors/niklas-een/)

1. **[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*
2. **[LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/yang-2023-leandojo-theorem-proving-retrieval-augmented/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kaiyu Yang, Aidan M. Swope, Alex Gu, et al.*2023 · 253 citations*
3. [**Generative Language Modeling for Automated Theorem Proving arXiv:2009.03393**Stanislas Polu, Ilya Sutskever*2020 · 241 citations*](https://md.research.elamur.ai/free/en/papers/polu-2020-generative-language-modeling-automated-theorem/)
4. [**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/)
5. **[A Survey of Machine Learning for Big Code and Naturalness](https://md.research.elamur.ai/free/en/papers/allamanis-2017-survey-machine-learning-big-code/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, et al.*2017 · 167 citations*
6. [**ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics arXiv:2302.12433**Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-proofnet-autoformalizing-formally-proving-undergraduate/)
7. **[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*
8. [**Formal Mathematics Statement Curriculum Learning arXiv:2202.01344**Stanislas Polu, Jesse Michael Han, Kunhao Zheng, et al.*2022 · 133 citations*](https://md.research.elamur.ai/free/en/papers/polu-2022-formal-mathematics-statement-curriculum-learning/)
9. [**History of Interactive Theorem Proving Handbook of the History of Logic, vol. 9 (Elsevier)**John Harrison, Josef Urban, Freek Wiedijk*2014 · 109 citations*](https://md.research.elamur.ai/free/en/papers/harrison-2014-history-interactive-theorem-proving/)
10. [**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/)
11. [**GamePad: A Learning Environment for Theorem Proving arXiv:1806.00608**Daniel Huang, Prafulla Dhariwal, Dawn Song, et al.*2018 · 94 citations*](https://md.research.elamur.ai/free/en/papers/huang-2018-gamepad-learning-environment-theorem-proving/)
12. [**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/)
13. [**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/)
14. [**LEGO-Prover: Neural Theorem Proving with Growing Libraries arXiv:2310.00656**Haiming Wang, Huajian Xin, Chuanyang Zheng, et al.*2023 · 79 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-lego-prover-neural-theorem-proving/)
15. [**Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers arXiv:2205.10893**Albert Q. Jiang, Wenda Li, Szymon Tworkowski, et al.*2022 · 79 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-thor-wielding-hammers-integrate-language/)
16. [**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/)
17. [**NaturalProofs: Mathematical Theorem Proving in Natural Language arXiv:2104.01112**Sean Welleck, Jiacheng Liu, Ronan Le Bras, et al.*2021 · 76 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2021-naturalproofs-mathematical-theorem-proving-natural/)
18. [**Generating Correctness Proofs with Neural Networks arXiv:1907.07794**Alex Sanchez-Stern, Yousef Alhessi, Lawrence Saul, et al.*2019 · 69 citations*](https://md.research.elamur.ai/free/en/papers/stern-2019-generating-correctness-proofs-neural-networks/)
19. **[ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E](https://md.research.elamur.ai/free/en/papers/chvalovsky-2019-enigma-ng-efficient-neural-gradient/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Karel Chvalovský, Jan Jakubův, Martin Suda, et al.*2019 · 61 citations*
20. [**Premise Order Matters in Reasoning with Large Language Models arXiv:2402.08939**Xinyun Chen, Ryan A. Chi, Xuezhi Wang, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-premise-order-matters-reasoning-large/)
21. [**Learning Continuous Semantic Representations of Symbolic Expressions arXiv:1611.01423**Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, et al.*2016 · 57 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2016-learning-continuous-semantic-representations-symbolic/)
22. [**Learning to Prove Theorems by Learning to Generate Theorems arXiv:2002.07019**Mingzhe Wang, Jia Deng*2020 · 53 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-learning-prove-theorems-learning-generate/)
23. [**Reinforcement Learning of Theorem Proving arXiv:1805.07563**Cezary Kaliszyk, Josef Urban, Henryk Michalewski, et al.*2018 · 50 citations*](https://md.research.elamur.ai/free/en/papers/kaliszyk-2018-reinforcement-learning-theorem-proving/)
24. [**Formal Mathematical Reasoning: A New Frontier in AI arXiv:2412.16075**Kaiyu Yang, Gabriel Poesia, Jingxuan He, et al.*2024 · 49 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-formal-mathematical-reasoning-new-frontier/)
25. [**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/)
26. [**Holophrasm: a neural Automated Theorem Prover for higher-order logic arXiv:1608.02644**Daniel Whalen*2016 · 48 citations*](https://md.research.elamur.ai/free/en/papers/whalen-2016-holophrasm-neural-automated-theorem-prover/)
27. [**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/)
28. **[Premise Selection in Natural Language Mathematical Texts](https://md.research.elamur.ai/free/en/papers/ferreira-2020-premise-selection-natural-language-mathematical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Deborah Ferreira, André Freitas*2020 · 44 citations*
29. [**IsarStep: a Benchmark for High-level Mathematical Reasoning arXiv:2006.09265**Wenda Li, Lei Yu, Yuhuai Wu, et al.*2020 · 43 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-isarstep-benchmark-high-level-mathematical/)
30. [**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/)
31. **[DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function](https://md.research.elamur.ai/free/en/papers/wang-2023-dt-solver-automated-theorem-proving/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haiming Wang, Ye Yuan, Zhengying Liu, et al.*2023 · 40 citations*
32. [**Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean arXiv:2404.12534**Peiyang Song, Kaiyu Yang, Anima Anandkumar*2024 · 39 citations*](https://md.research.elamur.ai/free/en/papers/song-2024-lean-copilot-large-language-models/)
33. [**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/)
34. [**Magnushammer: A Transformer-Based Approach to Premise Selection arXiv:2303.04488**Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/mikula-2023-magnushammer-transformer-based-approach-premise/)
35. [**Mathematical Reasoning in Latent Space arXiv:1909.11851**Dennis Lee, Christian Szegedy, Markus N. Rabe, et al.*2019 · 33 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-mathematical-reasoning-latent-space/)
36. **[Property Invariant Embedding for Automated Reasoning](https://md.research.elamur.ai/free/en/papers/olsak-2019-property-invariant-embedding-automated-reasoning/) [Frontiers in artificial intelligence and applications](https://md.research.elamur.ai/free/en/venues/frontiers-in-artificial-intelligence-and-applications/)**Miroslav Olšák, Cezary Kaliszyk, Josef Urban*2019 · 30 citations*
37. **[Learning Formal Mathematics From Intrinsic Motivation](https://md.research.elamur.ai/free/en/papers/poesia-2024-learning-formal-mathematics-intrinsic-motivation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Gabriel Poesia, David Broman, Nick Haber, et al.*2024 · 23 citations*
38. **[Stateful Premise Selection by Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/piotrowski-2020-stateful-premise-selection-recurrent-neural/) [EPiC series in computing](https://md.research.elamur.ai/free/en/venues/epic-series-in-computing/)**Bartosz Piotrowski, Josef Urban*2020 · 21 citations*
39. [**miniCTX: Neural Theorem Proving with (Long-)Contexts arXiv:2408.03350**Jiewen Hu, Thomas Zhu, Sean Welleck*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-minictx-neural-theorem-proving-long/)
40. [**Solving Formal Math Problems by Decomposition and Iterative Reflection arXiv:2507.15225**Yichi Zhou, Jianqiu Zhao, Yongxin Zhang, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2025-solving-formal-math-problems-decomposition/)
41. [**Guiding High-Performance SAT Solvers with Unsat-Core Predictions arXiv:1903.04671 (International Conference on Theory and Applications of Satisfiability Testing. Springer, Cham, 2019)**Daniel Selsam, Nikolaj Bjørner*2019 · 16 citations*](https://md.research.elamur.ai/free/en/papers/selsam-2019-guiding-high-performance-sat-solvers/)
42. [**Prover Agent: An Agent-Based Framework for Formal Mathematical Proofs arXiv:2506.19923**Kaito Baba, Chaoran Liu, Shuhei Kurita, et al.*2025 · 14 citations*](https://md.research.elamur.ai/free/en/papers/baba-2025-prover-agent-agent-based-framework/)
43. [**Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning arXiv:1807.08058**Gil Lederman, Markus N. Rabe, Edward A. Lee, et al.*2018 · 12 citations*](https://md.research.elamur.ai/free/en/papers/lederman-2018-learning-heuristics-quantified-boolean-formulas/)
44. [**Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics arXiv:2510.12787**Benjamin Breen, Marco Del Tredici, Jacob McCarran, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/breen-2025-ax-prover-deep-reasoning-agentic/)
45. **[MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective Search and Data Curation](https://md.research.elamur.ai/free/en/papers/liang-2025-mps-prover-advancing-stepwise-theorem/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhenwen Liang, Linfeng Song, Yang Li, et al.*2025 · 4 citations*

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

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