--- title: "Changyi Wan — author profile and papers — Elamur Research" description: "Changyi Wan: 2 papers on Elamur Research (2025), h-index 1 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/changyi-wan/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/changyi-wan/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Changyi Wan # Changyi Wan Changyi Wan has 2 papers in our library, in 2025, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Bin Wang](https://md.research.elamur.ai/free/en/authors/bin-wang/), [Guoqing Ma](https://md.research.elamur.ai/free/en/authors/guoqing-ma/), [Haoyang Huang](https://md.research.elamur.ai/free/en/authors/haoyang-huang/). 2 papers in our library · 2025 [OpenAlex](https://openalex.org/A5081111995) · [**h-index 1**](https://openalex.org/authors/A5081111995) · [9 works](https://openalex.org/works?filter=author.id:A5081111995) · [4 citations](https://openalex.org/works?filter=author.id:A5081111995&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/changyi-wan/#papers) · [🔗 Cited 102 times in our library](https://md.research.elamur.ai/free/en/authors/changyi-wan/cited-by/) · [⭐ Paper Stars ranking: #7896](https://md.research.elamur.ai/free/en/paper-stars/page/79/#star-7896) 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=Changyi+Wan) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Changyi+Wan) · [dblp](https://dblp.org/search?q=Changyi+Wan) · [ACL Anthology](https://aclanthology.org/search/?q=Changyi+Wan) ## Affiliations [🏢**StepFun AI** Shanghai · China 2024–2025 · 2 papers](https://md.research.elamur.ai/free/en/companies/stepfun-ai/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## 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/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Arts and Humanities · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Artificial Intelligence · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 1 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2025** 2 papers](https://md.research.elamur.ai/free/en/authors/changyi-wan/year/2025/) ## Papers The most cited first (OpenAlex and our library). 1. **[Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding](https://md.research.elamur.ai/free/en/papers/stepfun-2025-step-3-large-yet/)** *(2025 · 26 citations)* Decoding is the costliest stage of LLM serving per generated token, because hardware utilization is low. This matters most for reasoning models that produce long outputs. StepFun argues that parameter counts predict this cost poorly. Their paper presents Step-3, a 321B-parameter VLM that activates 38B parameters per… 2. **[Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model](https://md.research.elamur.ai/free/en/papers/ma-2025-step-video-t2v-technical/)** *(2025 · 21 citations)* Step-Video-T2V, from the StepFun Step-Video Team, is an open-source text-to-video model built to rival commercial systems such as Sora, Kling and Hailuo. ## Co-authors here 11 co-authors with a page, the most papers together first. - [Bin Wang](https://md.research.elamur.ai/free/en/authors/bin-wang/) - [Guoqing Ma](https://md.research.elamur.ai/free/en/authors/guoqing-ma/) - [Haoyang Huang](https://md.research.elamur.ai/free/en/authors/haoyang-huang/) - [Kun Yan](https://md.research.elamur.ai/free/en/authors/kun-yan/) - [Liangyu Chen](https://md.research.elamur.ai/free/en/authors/liangyu-chen/) - [Nan Duan](https://md.research.elamur.ai/free/en/authors/nan-duan/) - [Nuo Chen](https://md.research.elamur.ai/free/en/authors/nuo-chen/) - [Shengming Yin](https://md.research.elamur.ai/free/en/authors/shengming-yin/) - [Siyu Chen](https://md.research.elamur.ai/free/en/authors/siyu-chen/) - [Xing Chen](https://md.research.elamur.ai/free/en/authors/xing-chen/) - [Yu Zhou](https://md.research.elamur.ai/free/en/authors/yu-zhou/) ## Cited by Papers of our library cite their works 102 times (of the 149,439 papers whose bibliographies we read); [16 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/changyi-wan/cited-by/). Updated 1 November 2026