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Yi Shen

Yi Shen has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Kai Wang, Ning Wang, Shiguo Lian.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 19 · 83 works · 1,162 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 57 times in our library · ⭐ Paper Stars ranking: #9509

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

🎓Shanghai Jiao Tong UniversityShanghai · China2010–2026 · 44 paperscurrent🏥Shanghai Mental Health CenterShanghai · China2004–2026 · 42 paperscurrent🔬Shanghai Clinical Research CenterShanghai · China2020–2023 · 7 papers🔬Shanghai Key Laboratory of Psychotic DisordersShanghai · China2018–2023 · 6 papers🎓Fudan UniversityShanghai · China2004–2013 · 5 papers🏥Huashan HospitalShanghai · China2004–2013 · 5 papers🎓Chengdu University of Traditional Chinese MedicineChengdu · China2024–2025 · 3 papers🎓Guangzhou Xinhua UniversityGuangzhou2024–2025 · 3 papers🏥Ruijin HospitalShanghai · China2019–2025 · 3 papers🎓Zhejiang University of TechnologyHangzhou · China2012–2023 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models (2025 · 57 citations)
    Slow-thinking reasoning models such as DeepSeek-R1 tend to "overthink." They spend hundreds or thousands of tokens on trivial questions.
  2. Mixture of Heterogeneous Grouped Experts for Language Modeling (2026)
    Standard Mixture-of-Experts (MoE) language models use experts of one fixed size, so every token receives the same compute regardless of how hard it is to predict.

 Co-authors here

8 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 57 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated

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