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Shaotian Yan

Shaotian Yan has 3 papers in our library, in 2025–2026, most of them on Reasoning, Language models & pretraining; often with Chen Shen, Jieping Ye, Xiaosong Yuan.

3 papers in our library · 2025–2026

OpenAlex · h-index 4 · 11 works · 29 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 17 times in our library · ⭐ Paper Stars ranking: #8619

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Baoji University of Arts and SciencesXi'an · China2020–2025 · 6 paperscurrent🏢Alibaba GroupHangzhou · China2024–2025 · 2 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksComputer Vision and Pattern Recognition · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Shallow Focus, Deep Fixes: Enhancing Shallow Layers Vision Attention Sinks to Alleviate Hallucination in LVLMs (2025 · 14 citations)
    The paper investigates why large vision-language models (LVLMs) hallucinate, focusing on attention over image tokens, which make up most of the input.
  2. From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks (2025 · 12 citations)
    Large vision-language models (LVLMs) turn each image into hundreds of tokens before passing it to an LLM. How those visual tokens actually shape the generated answer has been unclear. The authors study this "information flow", meaning how strongly system, image and user-prompt tokens influence answer tokens, layer by…
  3. On the Step Length Confounding in LLM Reasoning Data Selection (2026 · 1 citation)
    The paper looks at naturalness-based selection of supervised fine-tuning data for long chain-of-thought reasoning.

 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 17 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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