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Yonggan Fu

Yonggan Fu has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Yingyan Celine Lin, Ameya Sunil Mahabaleshwarkar, Jan Kautz.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 16 · 84 works · 832 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 189 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7811

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

🎓Georgia Institute of TechnologyAtlanta · United States2023–2026 · 21 papersEducation 2023–2025 · Ph.D., School of Computer Sciencecurrent🏢NvidiaSanta Clara · United States2025–2026 · 5 paperscurrent🏢NvidiaReading · United Kingdom2026 · 2 paperscurrent🎓Rice UniversityHouston · United States2019–2022 · 32 papersEducation 2019–2022 · Ph.D., Department of Electrical and Computer Engineering
🏛️University of Science and TechnologyHefei · China2015–2019Education 2015–2019 · Bachelor of Science, School of the Gifted Young

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 6The 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 workInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Hymba: A Hybrid-head Architecture for Small Language Models (2024 · 50 citations)
    Small language models must balance accuracy against memory and speed. Transformers recall context well, but their compute cost and KV cache grow quickly with sequence length. State space models such as Mamba run cheaply but recall poorly. Earlier hybrids stacked the two layer types one after another, so a weak layer…
  2. Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time Compute (2026)
    Test-time compute lets small language models reason better by spending extra inference effort, but the two standard strategies have opposite costs.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Small Language Models are the Future of Agentic AI ↗arXiv:2506.02153 · no summary yetPeter Belcak et al.2025 · 92 citations
  2. Hymba: A Hybrid-head Architecture for Small Language ModelsarXiv:2411.13676Xin Dong, Yonggan Fu, Shizhe Diao, et al.2024 · 50 citations
  3. Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time ComputeFindings of the Association for Computational LinguisticsHyewon Suh, Chaojian Li, Cheng-Jhih Shih, et al.2026
  4. PROBE: PROcess-Based BEnchmark for Hallucination Detection ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYu Zhang et al.2026

 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 189 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

Updated

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