--- title: "Yihan Geng · Peking University — author profile and papers" description: "Yihan Geng, Peking University: 2 papers on Elamur Research (2024–2025), h-index 1 — affiliations, topics, co-authors and who cites them in…" canonical: "https://research.elamur.ai/free/en/authors/yihan-geng/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yihan-geng/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yihan Geng # Yihan Geng Yihan Geng has 2 papers in our library, in 2024–2025, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Bohan Lyu](https://md.research.elamur.ai/free/en/authors/bohan-lyu/), [Dahua Lin](https://md.research.elamur.ai/free/en/authors/dahua-lin/), [Haoyu Zhao](https://md.research.elamur.ai/free/en/authors/haoyu-zhao/). 2 papers in our library · 2024–2025 [OpenAlex](https://openalex.org/A5111223806) · [**h-index 1**](https://openalex.org/authors/A5111223806) · [3 works](https://openalex.org/works?filter=author.id:A5111223806) · [7 citations](https://openalex.org/works?filter=author.id:A5111223806&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/yihan-geng/#papers) · [🔗 Cited 141 times in our library](https://md.research.elamur.ai/free/en/authors/yihan-geng/cited-by/) · [⭐ Paper Stars ranking: #7158](https://md.research.elamur.ai/free/en/paper-stars/page/72/#star-7158) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Yihan+Geng) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yihan+Geng) · [dblp](https://dblp.org/search?q=Yihan+Geng) · [ACL Anthology](https://aclanthology.org/search/?q=Yihan+Geng) ## Affiliations [🎓**Peking University** Beijing · China 2025 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/peking-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computational Theory and Mathematics · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computational-theory-and-mathematics/) · [Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [Applied Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/applied-mathematics/) 🧭 Topics 1 Research subjects and languages the work is about.[Reasoning · 2 works](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/yihan-geng/year/2025/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/authors/yihan-geng/year/2024/) ## Papers The most cited first (OpenAlex and our library). 1. **[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/)** *(2024 · 80 citations)* Large language models do well on informal math but poorly on formal theorem proving in Lean. A main reason is the lack of formal training data: MiniF2F is small, and Mathlib mostly covers foundational theorems rather than competition problems. The authors built an active-learning autoformalization pipeline that… 2. **[Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction](https://md.research.elamur.ai/free/en/papers/lin-2025-goedel-prover-v2-scaling-formal/)** *(2025 · 67 citations)* Goedel-Prover-V2 is a family of open-source language models that write machine-checkable proofs in Lean 4. ## Co-authors here 11 co-authors with a page, the most papers together first. - [Bohan Lyu](https://md.research.elamur.ai/free/en/authors/bohan-lyu/) - [Dahua Lin](https://md.research.elamur.ai/free/en/authors/dahua-lin/) - [Haoyu Zhao](https://md.research.elamur.ai/free/en/authors/haoyu-zhao/) - [Huaiyuan Ying](https://md.research.elamur.ai/free/en/authors/huaiyuan-ying/) - [Jiayun Wu](https://md.research.elamur.ai/free/en/authors/jiayun-wu/) - [Jiri Gesi](https://md.research.elamur.ai/free/en/authors/jiri-gesi/) - [Kai Chen](https://md.research.elamur.ai/free/en/authors/kai-chen/) - [Shange Tang](https://md.research.elamur.ai/free/en/authors/shange-tang/) - [Yong Lin](https://md.research.elamur.ai/free/en/authors/yong-lin/) - [Zheng Yuan](https://md.research.elamur.ai/free/en/authors/zheng-yuan/) - [Zijian Wu](https://md.research.elamur.ai/free/en/authors/zijian-wu/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/): [NeurIPS 2024](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2024/) ## Cited by Papers of our library cite their works 141 times (of the 149,439 papers whose bibliographies we read); [18 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yihan-geng/cited-by/). Updated 2 November 2026