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Andrew Wen

Andrew Wen has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Guanchu Wang, Shaochen Zhong, Xia Hu.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 23 · 97 works · 1,426 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 161 times in our library · ⭐ Paper Stars ranking: #6003

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

🎓The University of Texas Health Science Center at HoustonHouston · United States2023–2026 · 26 paperscurrent🎓Rice UniversityHouston · United States2025–2026 · 3 paperscurrent🎓The University of Texas at AustinAustin · United States2026 · 2 paperscurrent🔬Mayo ClinicRochester · United States2018–2025 · 56 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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.Language models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models (2025 · 157 citations)
    Reasoning-focused LLMs such as OpenAI o1 and DeepSeek-R1 get better at math and coding by writing long Chain-of-Thought traces.
  2. FaithLM: Towards Faithful Explanations for Large Language Models (2026 · 9 citations)
    LLMs often explain their answers in natural language, but these explanations frequently do not reflect what actually drove the prediction. Classic fidelity tests, which delete important tokens, also fit poorly with free-text rationales whose content is semantic rather than token-level.

 Co-authors here

12 of 15 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 161 times (of the 149,439 papers whose bibliographies we read); 51 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/andrew-wen/