--- title: "MiniMax — author profile and papers — Elamur Research" description: "MiniMax: 2 papers on Elamur Research (2025), h-index 0 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/minimax/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/minimax/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / MiniMax # MiniMax MiniMax 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/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Aonian Li](https://md.research.elamur.ai/free/en/authors/aonian-li/), [Bangwei Gong](https://md.research.elamur.ai/free/en/authors/bangwei-gong/), [Bo Yang](https://md.research.elamur.ai/free/en/authors/bo-yang/). 2 papers in our library · 2025 [OpenAlex](https://openalex.org/A5115905944) · [**h-index 0**](https://openalex.org/authors/A5115905944) · [3 works](https://openalex.org/works?filter=author.id:A5115905944) · [0 citations](https://openalex.org/works?filter=author.id:A5115905944&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/minimax/#papers) · [🔗 Cited 116 times in our library](https://md.research.elamur.ai/free/en/authors/minimax/cited-by/) · [⭐ Paper Stars ranking: #6614](https://md.research.elamur.ai/free/en/paper-stars/page/67/#star-6614) 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=MiniMax) · [Semantic Scholar](https://www.semanticscholar.org/search?q=MiniMax) · [dblp](https://dblp.org/search?q=MiniMax) · [ACL Anthology](https://aclanthology.org/search/?q=MiniMax) ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [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/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) 🧭 Topics 2 Research subjects and languages the work is about.[Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) · [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/minimax/year/2025/) ## Papers The most cited first (OpenAlex and our library). 1. **[MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-m1-scaling-test/)** *(2025 · 66 citations)* Reasoning models get better when they generate longer chains of thought. Standard softmax attention, however, has quadratic cost in sequence length, which makes long outputs expensive. MiniMax-M1 tackles this with an open-weight reasoning model built on a hybrid design. It starts from MiniMax-Text-01, a… 2. **[MiniMax-01: Scaling Foundation Models with Lightning Attention](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-01-scaling-foundation/)** *(2025 · 50 citations)* Most current LLMs and VLMs handle 32K–256K tokens of context. That is often too little for tasks like reading whole books, working across entire code repositories, or many-shot in-context learning. The main obstacle is that softmax attention cost grows quadratically with length. MiniMax-01 addresses this with a… ## Co-authors here 11 co-authors with a page, the most papers together first. - [Aonian Li](https://md.research.elamur.ai/free/en/authors/aonian-li/) ×2 - [Bangwei Gong](https://md.research.elamur.ai/free/en/authors/bangwei-gong/) ×2 - [Bo Yang](https://md.research.elamur.ai/free/en/authors/bo-yang/) ×2 - [Boji Shan](https://md.research.elamur.ai/free/en/authors/boji-shan/) ×2 - [Cheng Zhu](https://md.research.elamur.ai/free/en/authors/cheng-zhu/) ×2 - [Aili Chen](https://md.research.elamur.ai/free/en/authors/aili-chen/) - [Chang Liu](https://md.research.elamur.ai/free/en/authors/chang-liu/) - [Chao Wang](https://md.research.elamur.ai/free/en/authors/chao-wang/) - [Chengjun Xiao](https://md.research.elamur.ai/free/en/authors/chengjun-xiao/) - [Da Chen](https://md.research.elamur.ai/free/en/authors/da-chen/) - [Dong Li](https://md.research.elamur.ai/free/en/authors/dong-li/) ## Cited by Papers of our library cite their works 116 times (of the 149,439 papers whose bibliographies we read); [38 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/minimax/cited-by/). Updated 1 November 2026