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Qintong Zhang

Qintong Zhang has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Vision & language; often with Bin Wang, Conghui He, Fan Wu.

2 papers in our library · 2025–2026

OpenAlex · h-index 8 · 20 works · 389 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 49 times in our library · ⭐ Paper Stars ranking: #7167

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

🎓Peking UniversityBeijing · China2024–2026 · 5 paperscurrent🔬Institute of PhysicsBeijing · China2015–2020 · 7 papers🏛️Chinese Academy of SciencesBeijing · China2015–2020 · 6 papers🏢NAURABeijing · China2024 · 2 papers🔬State Key Laboratory of MagnetismBeijing2015 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksVision & language · 2 works

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Stop Looking for “Important Tokens” in Multimodal Language Models: Duplication Matters More (2025 · 44 citations)
    Vision tokens make up most of the input to multimodal LLMs, which makes inference expensive. Most training-free pruning methods score each token's "importance" from attention maps and drop the low scorers. The authors argue this approach has four weaknesses. It treats scores as fixed even after other tokens are…
  2. MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026 · 35 citations)
    Turning document images into structured text is difficult because pages are high-resolution, dense, and complex in layout.

 Co-authors here

12 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 49 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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