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Beijun Shen

Beijun Shen has 2 papers in our library, in 2026, most of them on Language models & pretraining, Question answering; often with Xiaodong Gu, Yuling Shi, Yuhang Wang.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 19 · 150 works · 1,134 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 6 times in our library · ⭐ Paper Stars ranking: #10129

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 · China2006–2026 · 131 paperscurrent🎓East China University of Science and TechnologyShanghai · China2005–2015 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workQuestion answering · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. SWE-QA: Can Language Models Answer Repository-level Code Questions? (2026 · 7 citations)
    Most code question-answering benchmarks, such as CoSQA and CodeQA, test models on short, isolated snippets. Real developer questions often require moving across files, following dependencies and understanding a project's architecture. SWE-QA is a benchmark built for this repository-level setting.
  2. GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts (2026 · 1 citation)
    Large reasoning models produce long chains of thought, which makes inference slow and expensive. Collaborative inference addresses this by splitting work between a small and a large model. The hard part is deciding cheaply which model should handle each step. Existing step-level routers either generate a full step…

 Co-authors here

3 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 6 times (of the 149,439 papers whose bibliographies we read).

Updated

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