--- title: "Xiaoxi Jiang · Alibaba Group — author profile and papers" description: "Xiaoxi Jiang, Alibaba Group: 2 papers on Elamur Research (2026), h-index 1 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/xiaoxi-jiang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/xiaoxi-jiang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Xiaoxi Jiang # Xiaoxi Jiang Xiaoxi Jiang 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/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Jingwei Ni](https://md.research.elamur.ai/free/en/authors/jingwei-ni/), [Mengyu Zhou](https://md.research.elamur.ai/free/en/authors/mengyu-zhou/), [Yihao Liu](https://md.research.elamur.ai/free/en/authors/yihao-liu/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5121672896) · [**h-index 1**](https://openalex.org/authors/A5121672896) · [17 works](https://openalex.org/works?filter=author.id:A5121672896) · [1 citations](https://openalex.org/works?filter=author.id:A5121672896&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/xiaoxi-jiang/#papers) · [🔗 Cited 11 times in our library](https://md.research.elamur.ai/free/en/authors/xiaoxi-jiang/cited-by/) · [⭐ Paper Stars ranking: #11810](https://md.research.elamur.ai/free/en/paper-stars/page/119/#star-11810) 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=Xiaoxi+Jiang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Xiaoxi+Jiang) · [dblp](https://dblp.org/search?q=Xiaoxi+Jiang) · [ACL Anthology](https://aclanthology.org/search/?q=Xiaoxi+Jiang) ## Affiliations [🏢**Alibaba Group** Hangzhou · China 2025–2026 · 13 papers*current*](https://md.research.elamur.ai/free/en/companies/alibaba-group/) 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).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [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/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [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.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/xiaoxi-jiang/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills](https://md.research.elamur.ai/free/en/papers/ni-2026-trace2skill-distill-trajectory-local-lessons/)** *(2026 · 12 citations)* LLM agents increasingly depend on skill files, which are reusable documents describing procedures for a domain. 2. **[RiT: Rubrics-in-Thinking Reinforcement Learning for Improved Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/tian-2026-rit-rubrics-thinking-reinforcement-learning/)** *(2026)* Most reinforcement learning for large reasoning models rewards only the final answer. This can let flawed or shortcut reasoning go unpunished. Process reward models address this mainly in checkable domains such as math or code. RiT (Rubrics-in-Thinking RL) instead brings rubric-based supervision to the thinking trace… ## Co-authors here 4 co-authors with a page, the most papers together first. - [Jingwei Ni](https://md.research.elamur.ai/free/en/authors/jingwei-ni/) - [Mengyu Zhou](https://md.research.elamur.ai/free/en/authors/mengyu-zhou/) - [Yihao Liu](https://md.research.elamur.ai/free/en/authors/yihao-liu/) - [Yutao Sun](https://md.research.elamur.ai/free/en/authors/yutao-sun/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [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 11 times (of the 149,439 papers whose bibliographies we read); [2 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/xiaoxi-jiang/cited-by/). Updated 3 November 2026