--- title: "Who cites MUSTARD: Mastering Uniform — 29 citing papers" description: "29 papers of the Elamur Research library cite «MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data», found in their…" canonical: "https://research.elamur.ai/free/en/papers/huang-2024-mustard-mastering-uniform-synthesis-theorem/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/huang-2024-mustard-mastering-uniform-synthesis-theorem/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [MUSTARD: Mastering Uniform Synthesis of Theorem…](https://md.research.elamur.ai/free/en/papers/huang-2024-mustard-mastering-uniform-synthesis-theorem/) / cited by # Who cites «MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data» 29 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 «MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data»](https://md.research.elamur.ai/free/en/papers/huang-2024-mustard-mastering-uniform-synthesis-theorem/) 1. **[AlphaMath Almost Zero: Process Supervision without Process](https://md.research.elamur.ai/free/en/papers/chen-2024-alphamath-almost-zero-process-supervision/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guoxin Chen, Minpeng Liao, Chengxi Li, et al.*2024 · 120 citations* 2. [**DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data arXiv:2405.14333**Huajian Xin, Daya Guo, Zhihong Shao, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/xin-2024-deepseek-prover-advancing-theorem-proving/) 3. [**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/) 4. **[Lean Workbook: A large-scale Lean problem set formalized from natural language math problems](https://md.research.elamur.ai/free/en/papers/ying-2024-lean-workbook-large-scale-lean/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Huaiyuan Ying, Zijian Wu, Yihan Geng, et al.*2024 · 80 citations* 5. [**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/) 6. [**Lean-STaR: Learning to Interleave Thinking and Proving arXiv:2407.10040**Haohan Lin, Zhiqing Sun, Sean Welleck, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-lean-star-learning-interleave-thinking/) 7. [**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/) 8. [**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/) 9. **[Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/huang-2024-key-point-driven-data-synthesis/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yiming Huang, Xiao Liu, Yeyun Gong, et al.*2024 · 45 citations* 10. [**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/) 11. [**DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning arXiv:2310.02954**Jing Xiong, Zixuan Li, Chuanyang Zheng, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2023-dq-lore-dual-queries-low/) 12. **[Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective](https://md.research.elamur.ai/free/en/papers/yu-2025-chain-reasoning-unified-mathematical-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, et al.*2025 · 23 citations* 13. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations* 14. [**The Limits of Inference Scaling Through Resampling arXiv:2411.17501**Benedikt Stroebl, Sayash Kapoor, Arvind Narayanan*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/stroebl-2024-limits-inference-scaling-through-resampling/) 15. **[Proving Theorems Recursively](https://md.research.elamur.ai/free/en/papers/wang-2024-proving-theorems-recursively/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haiming Wang, Huajian Xin, Zhengying Liu, et al.*2024 · 19 citations* 16. [**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/) 17. **[Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification](https://md.research.elamur.ai/free/en/papers/liu-2025-safe-enhancing-mathematical-reasoning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chengwu Liu, Ye Yuan, Yichun Yin, et al.*2025 · 17 citations* 18. **[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* 19. **[RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library](https://md.research.elamur.ai/free/en/papers/wang-2026-rv-syn-rational-verifiable-mathematical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, et al.*2026* 10 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/huang-2024-mustard-mastering-uniform-synthesis-theorem/) — MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data