🧑‍🔬 Who cites Lionel Ming-shuan Ni

106 citations of their works from 103 papers of our library (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · ← Lionel Ming-shuan Ni

1. Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object DetectionarXiv:2303.05499Shilong Liu, Zhaoyang Zeng, Tianhe Ren, et al.2023 · 472 citations
2. An Empirical Survey of Data Augmentation for Limited Data Learning in NLPTransactions of the Association for Computational Linguistics (TACL)Jiaao Chen, Derek Tam, Colin Raffel, et al.2023 · 254 citations
3. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
4. Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4VarXiv:2310.11441Jianwei Yang, Hao Zhang, Feng Li, et al.2023 · 137 citations
5. Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language ModelsarXiv:2501.09686Fengli Xu, Qianyue Hao, Zefang Zong, et al.2025 · 120 citations
6. HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMsarXiv:2412.18925Junying Chen, Zhenyang Cai, Ke Ji, et al.2024 · 87 citations
7. A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?arXiv:2503.24235Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.2025 · 79 citations
8. Spurious Rewards: Rethinking Training Signals in RLVRarXiv:2506.10947Rulin Shao, Shuyue Stella Li, Rui Xin, et al.2025 · 53 citations
9. Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time ScalingarXiv:2502.06703Runze Liu, Junqi Gao, Jian Zhao, et al.2025 · 41 citations
10. Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?Annual Meeting of the Association for Computational Linguistics (ACL)Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.2025 · 38 citations
11. A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super IntelligencearXiv:2507.21046Huan-ang Gao, Jiayi Geng, Wenyue Hua, et al.2025 · 36 citations
12. Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning PerspectivearXiv:2412.14135Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.2024 · 33 citations
13. R-PRM: Reasoning-Driven Process Reward ModelingConference on Empirical Methods in Natural Language Processing (EMNLP)Shuaijie She, Junxiao Liu, Yifeng Liu, et al.2025 · 18 citations
14. Aviary: training language agents on challenging scientific tasksarXiv:2412.21154Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, et al.2024 · 18 citations
15. CodePRM: Execution Feedback-enhanced Process Reward Model for Code GenerationFindings of the Association for Computational LinguisticsQingyao Li, Xinyi Dai, Xiangyang Li, et al.2025 · 15 citations
16. Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RLarXiv:2505.17952Che Liu, Haozhe Wang, Jiazhen Pan, et al.2025 · 10 citations
17. Examining False Positives under Inference Scaling for Mathematical ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Yu Wang, Nan Yang, Liang Wang, et al.2025 · 9 citations
18. ViLBench: A Suite for Vision-Language Process Reward ModelingConference on Empirical Methods in Natural Language Processing (EMNLP)Haoqin Tu, Weitao Feng, Hardy Chen, et al.2025 · 9 citations
19. MetaScale: Test-Time Scaling with Evolving Meta-ThoughtsFindings of the Association for Computational LinguisticsQin Liu, Wenxuan Zhou, Nan Xu, et al.2026 · 7 citations
20. Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic ConfidenceAnnual Meeting of the Association for Computational Linguistics (ACL)Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, et al.2026 · 4 citations
21. Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level RewardsAnnual Meeting of the Association for Computational Linguistics (ACL)Raffaele Pisano, Roberto Navigli2026 · 1 citation
22. A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and UsageAnnual Meeting of the Association for Computational Linguistics (ACL)Congmin Zheng, Jiachen Zhu, Zhuoying Ou, et al.2026
23. GigaCheck: Detecting LLM-generated Content via Object-Centric Span LocalizationFindings of the Association for Computational LinguisticsIrina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, et al.2026
24. Model-Based Imaginative Planning for Embodied AgentsAnnual Meeting of the Association for Computational Linguistics (ACL)Junru Song, Hengzhe Jin, Yucong Huang, et al.2026
25. ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, et al.2026

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