Ziyu Liu: h-index, Total Citations, and Citation Map
Ziyu Liu's h-index is 11 (11 i10-index, 933+ total citations across 16+ publications) according to Google Scholar as of May 2026. Ziyu Liu is affiliated with Shanghai Jiao Tong University.
Ziyu Liu is a researcher affiliated with Shanghai Jiao Tong University, specializing in MLLM, RFT, Agent. Their work has been cited 933 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Ziyu Liu's Citation Metrics
Bibliometric impact based on 16 indexed publications.
- H-Index
- 11
- i10-Index
- 11
- Total Citations
- 933
- Citing Countries
- 19
As of May 2026.
Ziyu Liu has an h-index of 11 and 933 total citations across 16 publications, with research cited by institutions in 19 countries.
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Global Impact Map
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Top Cited Works
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Visual-rft: Visual reinforcement fine-tuning
2025471
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
22 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced large vision-language model alignment by introducing multi-image augmented direct preference optimization and subsequent visual reinforcement fine-tuning methods.
The researcher established a rigorous benchmark for evaluating long-context document understanding capabilities, specifically addressing the critical challenge of integrating visualizations within extended textual contexts.
The researcher developed Rar, a framework for retrieving and ranking augmented multimodal large language models to enhance visual recognition capabilities.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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