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XiaoFeng Wang

XiaoFeng Wang has 2 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Ang Li, Haixu Tang, Rujing Yao.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 42 · 243 works · 6,367 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #14006

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

🎓Indiana University BloomingtonBloomington · United States2004–2026 · 193 papers🎓Indiana UniversityBloomington · United States2004–2026 · 95 papers🎓Nanyang Technological UniversitySingapore · Singapore2025–2026 · 41 papers🎓Carnegie Mellon UniversityPittsburgh · United States2002–2004 · 5 papers🎓Shanghai Jiao Tong UniversityShanghai · China2021–2025 · 2 papers🎓Georgia Institute of TechnologyAtlanta · United States2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 18 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).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workHardware and Architecture · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition (2026)
    The paper addresses a dilemma in using LLMs for sensitive work. Cloud-hosted models such as GPT-5 give strong answers, but sending them raw queries can reveal confidential research or legal intent. Purely local models keep data private but answer less well. The authors propose GTKA, a client-side, semantic-level…
  2. ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks (2026)
    Reasoning-capable LLMs are costly to build. A competitor can still copy their step-by-step behavior by querying a deployed model and training a student on its outputs, including the reasoning traces. Conventional text watermarks (token-level logit biases or sampling rules) tend to disappear in this process, because…

 Co-authors here

10 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.

Updated

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