--- title: "Chuan Xiao — author profile and papers — Elamur Research" description: "Chuan Xiao, Osaka University of Economics: 2 papers on Elamur Research (2026), h-index 0 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/chuan-xiao/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/chuan-xiao/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Chuan Xiao # Chuan Xiao Chuan Xiao has 2 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Jianbin Qin](https://md.research.elamur.ai/free/en/authors/jianbin-qin/), [Rui Mao](https://md.research.elamur.ai/free/en/authors/rui-mao/), [Yuyang Dong](https://md.research.elamur.ai/free/en/authors/yuyang-dong/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5133936218) · [**h-index 0**](https://openalex.org/authors/A5133936218) · [9 works](https://openalex.org/works?filter=author.id:A5133936218) · [0 citations](https://openalex.org/works?filter=author.id:A5133936218&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/chuan-xiao/#papers) · 🔗 Cited 1 time in our library · [⭐ Paper Stars ranking: #11463](https://md.research.elamur.ai/free/en/paper-stars/page/115/#star-11463) 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=Chuan+Xiao) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Chuan+Xiao) · [dblp](https://dblp.org/search?q=Chuan+Xiao) · [ACL Anthology](https://aclanthology.org/search/?q=Chuan+Xiao) ## Affiliations [🎓**The University of Osaka** Osaka · Japan 2026 · 5 papers*current*](https://md.research.elamur.ai/free/en/universities/the-university-of-osaka/) · [🎓**Osaka University of Economics** Osaka · Japan 2026 · 1 paper*current*](https://ror.org/04g11bp59) 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).[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/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 2 Research subjects and languages the work is about.[Efficient models & compression · 2 works](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/chuan-xiao/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Adaptive Layer Selection for Layer-Wise Token Pruning in LLM Inference](https://md.research.elamur.ai/free/en/papers/taniguchi-2026-adaptive-layer-selection-layer-wise/)** *(2026 · 1 citation)* Layer-wise token pruning reduces the KV cache during LLM inference. It keeps only a subset of important tokens beyond a certain Transformer layer. Existing methods such as FastKV and GemFilter fix this "selection layer" in advance, regardless of the task. A layer that suits easy QA can therefore fail badly on harder… 2. **[GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-grasprune-global-gating-budgeted-structured/)** *(2026)* Serving large language models is costly because weights, attention computation and the KV cache all consume memory and add latency. ## Co-authors here 4 co-authors with a page, the most papers together first. - [Jianbin Qin](https://md.research.elamur.ai/free/en/authors/jianbin-qin/) - [Rui Mao](https://md.research.elamur.ai/free/en/authors/rui-mao/) - [Yuyang Dong](https://md.research.elamur.ai/free/en/authors/yuyang-dong/) - [Ziyang Wang](https://md.research.elamur.ai/free/en/authors/ziyang-wang/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ## Cited by Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read). Updated 1 November 2026