🧑‍🔬 Zhihao Fan

🏛️ Affiliations

🎓Lanzhou Jiaotong UniversityLanzhou · China2025–2026 · 3 paperscurrent🏥Qilu Hospital of Shandong UniversityJinan · China2026 · 1 papercurrent🎓Shandong UniversityJinan · China2026 · 1 papercurrent🎓Fudan UniversityShanghai · China2018–2024 · 22 papers🎓University of Shanghai for Science and TechnologyShanghai · China2022–2023 · 6 papers🏢Alibaba GroupHangzhou · China2024–2025 · 5 papers🎓South China University of TechnologyGuangzhou · China2020–2023 · 5 papers🎓Central University of Finance and EconomicsBeijing · China2022–2024 · 3 papers🎓Hangzhou Dianzi UniversityHangzhou · China2010 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution (2024 · 860 citations)
   Most large vision-language models squeeze every picture into one fixed input size, such as 224×224. This discards fine detail in large images. Most of them also give positions as a flat one-dimensional sequence, which fits poorly with the 2D layout of images and the time axis of video. Qwen2-VL, from the Qwen Team at…
2. AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025 · 36 citations)
   Medical QA benchmarks give a model the whole case at once. Real diagnosis is different: a clinician must gather facts step by step through conversation and tests. To measure this skill, the authors built AI Hospital, a multi-agent simulator. An LLM under test plays the Doctor, while GPT-3.5 drives two non-player…
3. Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025 · 30 citations)
   Static benchmarks age quickly as LLMs improve. Their test sets may also leak into training data, which inflates scores. Rather than annotating new datasets from scratch, the authors update existing ones automatically. Their framework has three goals:

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any ResolutionarXiv:2409.12191Peng Wang, Shuai Bai, Sinan Tan, et al.2024 · 860 citations
2. Mask Attention Networks: Rethinking and Strengthen Transformer ↗Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetZhihao Fan et al.2021 · 69 citations
3. AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction SimulatorInternational Conference on Computational Linguistics (COLING)Zhihao Fan, Lai Wei, Jialong Tang, et al.2025 · 36 citations
4. Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationInternational Conference on Computational Linguistics (COLING)Siyuan Wang, Zhuohan Long, Zhihao Fan, et al.2025 · 30 citations

🤝 Co-authors here

12 of 19 co-authors with a page, the most papers together first.

• Zhongyu Wei ×2
• Fei Huang
• Jialin Wang
• Jialong Tang
• Jinze Bai
• Kai Dang
• Keqin Chen
• Lai Wei
• Peng Wang
• Shijie Wang
• Shuai Bai
• Sinan Tan

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• International Conference on Computational Linguistics (COLING) ×2: COLING 2025 ×2
• Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

🔗 Cited by

Papers of our library cite their works 841 times (of the 149,439 papers whose bibliographies we read); 163 citing papers with a page here →.

Updated 1 November 2026

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