Yi Peng
Yi Peng has 3 papers in our library, in 2025, most of them on Reasoning, Vision & language; often with Jiangbo Pei, Peiyu Wang, Yunzhuo Hao.
OpenAlex · ORCID · h-index 22 · 94 works · 2,277 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 33 times in our library · ⭐ Paper Stars ranking: #8344
Affiliations
🏢Pangang Group Panzhihua Iron and Steel Research InstitutePanzhihua ·
China2020–2025 · 6 papers🎓Chinese University of Hong Kong, ShenzhenShenzhen ·
China2024–2025 · 3 papers🎓Huazhong University of Science and TechnologyWuhan ·
China2025 · 2 papers🏥Hubei Cancer HospitalWuhan ·
China2025 · 2 papers🏛️Chinese Academy of Tropical Agricultural SciencesHaikou ·
China2025 · 1 paper🏥Fourth Hospital of Hebei Medical UniversityShijiazhuang ·
China2025 · 1 paper🎓Hunan UniversityChangsha ·
China2025 · 1 paper🏛️Ministry of Natural ResourcesBeijing ·
China2025 · 1 paper🏛️National Administration of Surveying, Mapping and Geoinformation of ChinaBeijing ·
China2025 · 1 paper🎓Tianjin University of Technology and EducationTianjin ·
China2025 · 1 paper
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 works
🧭 Topics 2Research subjects and languages the work is about.Reasoning · 3 worksVision & language · 3 works
Works by year
2020s
Papers
- Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought (2025 · 17 citations)
Vision-language models describe images well but fall behind text-only reasoning LLMs on multi-step tasks such as geometry proofs. - Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning (2025 · 10 citations)
"Slow-thinking" vision-language models tend to improve on math and science benchmarks, but they often lose accuracy on everyday perception tasks and hallucinate more about image content. - Skywork-R1V3 Technical Report (2025 · 6 citations)
Open-source vision-language models still trail proprietary systems on complex visual reasoning, partly because high-quality multimodal annotations are scarce.
Co-authors here
- Jiangbo Pei ×3
- Peiyu Wang ×3
- Yunzhuo Hao ×3
- Jianhao Zhang ×2
- Tianyidan Xie ×2
- Wei Shen ×2
- Weijie Qiu ×2
- Xiaokun Wang ×2
- Xuchen Song ×2
- Yang Liu ×2
- Yichen Wei ×2
- Jian Peng
Cited by
Updated
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