---
title: "Yan Shu — author profile and papers — Elamur Research"
description: "Yan Shu, Shanghai Jiao Tong University: 2 papers on Elamur Research (2024–2026), h-index 3 — affiliations, topics, co-authors and who…"
canonical: "https://research.elamur.ai/free/en/authors/yan-shu/"
---
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# Yan Shu

Yan Shu has 2 papers in our library, in 2024–2026, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Bo Zhang](https://md.research.elamur.ai/free/en/authors/bo-zhang/), [Bo Zhao](https://md.research.elamur.ai/free/en/authors/bo-zhao/), [Boya Wu](https://md.research.elamur.ai/free/en/authors/boya-wu/).

2 papers in our library · 2024–2026

[OpenAlex](https://openalex.org/A5102605916) · [**h-index 3**](https://openalex.org/authors/A5102605916) · [7 works](https://openalex.org/works?filter=author.id:A5102605916) · [91 citations](https://openalex.org/works?filter=author.id:A5102605916&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/yan-shu/#papers) · [🔗 Cited 98 times in our library](https://md.research.elamur.ai/free/en/authors/yan-shu/cited-by/) · [⭐ Paper Stars ranking: #7261](https://md.research.elamur.ai/free/en/paper-stars/page/73/#star-7261)

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=Yan+Shu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yan+Shu) · [dblp](https://dblp.org/search?q=Yan+Shu) · [ACL Anthology](https://aclanthology.org/search/?q=Yan+Shu)

## Affiliations

[🔬**Beijing Academy of Artificial Intelligence** Beijing · China 2025 · 1 paper*current*](https://md.research.elamur.ai/free/en/labs/beijing-academy-of-artificial-intelligence/) · [🎓**Shanghai Jiao Tong University** Shanghai · China 2025 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions 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/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Artificial Intelligence · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Health Informatics · 1 work](https://md.research.elamur.ai/free/en/shelves/medicine/health-informatics/) · [Medicine · 1 work](https://md.research.elamur.ai/free/en/shelves/medicine/)

🧭 Topics 1 Research subjects and languages the work is about.[Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/)

## Works by year

### 2020s

[**2026** 1 paper](https://md.research.elamur.ai/free/en/authors/yan-shu/year/2026/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/authors/yan-shu/year/2024/)

## Papers

The most cited first (OpenAlex and our library).

1. **[MLVU: Benchmarking Multi-task Long Video Understanding](https://md.research.elamur.ai/free/en/papers/zhou-2024-mlvu-benchmarking-multi-task-long/)** *(2024 · 140 citations)* Benchmarks for evaluating how multimodal LLMs (MLLMs) handle long videos have three main gaps. Most use clips lasting only seconds, and many cover a single genre or task. Their questions can also often be answered from one frame or from general knowledge of famous films. MLVU addresses these gaps with 1,730 videos…
2. **[Scaling is Not All You Need: Clinical-Oriented Reinforcement Learning Makes Parameter-Efficient Clinical Reasoning](https://md.research.elamur.ai/free/en/papers/liu-2026-scaling-is-not-all-you/)** *(2026)* Strong medical reasoning in LLMs usually depends on very large models and costly hand-annotated chain-of-thought data.

## Co-authors here

12 co-authors with a page, the most papers together first.

- [Bo Zhang](https://md.research.elamur.ai/free/en/authors/bo-zhang/)
- [Bo Zhao](https://md.research.elamur.ai/free/en/authors/bo-zhao/)
- [Boya Wu](https://md.research.elamur.ai/free/en/authors/boya-wu/)
- [Bryan Dai](https://md.research.elamur.ai/free/en/authors/bryan-dai/)
- [Chi Liu](https://md.research.elamur.ai/free/en/authors/chi-liu/)
- [Hongming Piao](https://md.research.elamur.ai/free/en/authors/hongming-piao/)
- [Junjie Zhou](https://md.research.elamur.ai/free/en/authors/junjie-zhou/)
- [Minghao Qin](https://md.research.elamur.ai/free/en/authors/minghao-qin/)
- [Shitao Xiao](https://md.research.elamur.ai/free/en/authors/shitao-xiao/)
- [Xi Yang](https://md.research.elamur.ai/free/en/authors/xi-yang/)
- [Yongping Xiong](https://md.research.elamur.ai/free/en/authors/yongping-xiong/)
- [Zheng Liu](https://md.research.elamur.ai/free/en/authors/zheng-liu/)

## 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/)
- [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/): [IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2024](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/2024/)

## Cited by

Papers of our library cite their works 98 times (of the 149,439 papers whose bibliographies we read); [30 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yan-shu/cited-by/).

Updated 2 November 2026
