--- title: "Who cites Generating Correctness Proofs with Neural Networks" description: "22 papers of the Elamur Research library cite «Generating Correctness Proofs with Neural Networks», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/stern-2019-generating-correctness-proofs-neural-networks/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/stern-2019-generating-correctness-proofs-neural-networks/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Generating Correctness Proofs with Neural Networks](https://md.research.elamur.ai/free/en/papers/stern-2019-generating-correctness-proofs-neural-networks/) / cited by # Who cites «Generating Correctness Proofs with Neural Networks» 22 papers of our library cite it (of the 149,439 whose bibliographies we read) · 15 with a page here, the most cited first · [← back to «Generating Correctness Proofs with Neural Networks»](https://md.research.elamur.ai/free/en/papers/stern-2019-generating-correctness-proofs-neural-networks/) 1. [**Baldur: Whole-Proof Generation and Repair with Large Language Models arXiv:2303.04910**Emily First, Markus N. Rabe, Talia Ringer, et al.*2023 · 175 citations*](https://md.research.elamur.ai/free/en/papers/first-2023-baldur-whole-proof-generation-repair/) 2. **[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* 3. [**Proof Artifact Co-training for Theorem Proving with Language Models arXiv:2102.06203**Jesse Michael Han, Jason Rute, Yuhuai Wu, et al.*2021 · 82 citations*](https://md.research.elamur.ai/free/en/papers/han-2021-proof-artifact-co-training-theorem/) 4. [**Mathematical Reasoning via Self-supervised Skip-tree Training arXiv:2006.04757**Markus N. Rabe, Dennis Lee, Kshitij Bansal, et al.*2020 · 54 citations*](https://md.research.elamur.ai/free/en/papers/rabe-2020-mathematical-reasoning-self-supervised-skip/) 5. [**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/) 6. [**An In-Context Learning Agent for Formal Theorem-Proving arXiv:2310.04353**Amitayush Thakur, George Tsoukalas, Yeming Wen, et al.*2023 · 47 citations*](https://md.research.elamur.ai/free/en/papers/thakur-2023-context-learning-agent-formal-theorem/) 7. [**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/) 8. [**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/) 9. [**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/) 10. [**Passport: Improving Automated Formal Verification Using Identifiers arXiv:2204.10370 (ACM Transactions on Programming Languages and Systems (TOPLAS), 45(2):12:1-12:30, June 2023)**Alex Sanchez-Stern, Emily First, Timothy Zhou, et al.*2022 · 35 citations*](https://md.research.elamur.ai/free/en/papers/stern-2022-passport-improving-automated-formal-verification/) 11. **[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* 12. **[AutoVerus: Automated Proof Generation for Rust Code](https://md.research.elamur.ai/free/en/papers/yang-2024-autoverus-automated-proof-generation-rust/) [Proceedings of the ACM on Programming Languages](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-acm-on-programming-languages/)**Chenyuan Yang, Xuheng Li, Md Rakib Hossain Misu, et al.*2024 · 28 citations* 13. [**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/) 14. [**Graph2Tac: Online Representation Learning of Formal Math Concepts arXiv:2401.02949**Lasse Blaauwbroek, Miroslav Olšák, Jason Rute, et al.*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/blaauwbroek-2024-graph2tac-online-representation-learning-formal/) 15. [**Can Transformers Learn to Solve Problems Recursively? arXiv:2305.14699**Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-transformers-learn-solve-problems-recursively/) 7 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/stern-2019-generating-correctness-proofs-neural-networks/) — Generating Correctness Proofs with Neural Networks