Fine-tuning MLLMs Without Forgetting Is Easier Than You Think.

Kaifeng Lyu

He Li Yuhui Zhang Xiaohan Wang Kaifeng Lyu Serena Yeung-Levy Fine-tuning MLLMs Without Forgetting Is Easier Than You Think. 2026 March abs/2603.14493 CoRR https://doi.org/10.48550/arXiv.2603.14493 db/journals/corr/corr2603.html#abs-2603-14493 streams/journals/corr

Kexian Tang Jiani Wang Shaowen Wang Kaifeng Lyu SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection. 2026 March abs/2603.22213 CoRR https://doi.org/10.48550/arXiv.2603.22213 db/journals/corr/corr2603.html#abs-2603-22213 streams/journals/corr

Binghui Li Zhixuan Pan Kaifeng Lyu Jian Li Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks. 2025 ICLR https://openreview.net/forum?id=zPHra4V5Mc conf/iclr/2025 db/conf/iclr/iclr2025.html#LiPLL25 Rui Lu 0001 Runzhe Wang Kaifeng Lyu Xitai Jiang Gao Huang 0001 Mengdi Wang 0001 Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias. 2025 ICLR https://openreview.net/forum?id=SKW10XJlAI conf/iclr/2025 db/conf/iclr/iclr2025.html#LuWLJ0W25 Kairong Luo Haodong Wen Shengding Hu Zhenbo Sun Zhiyuan Liu 0001 Maosong Sun 0001 Kaifeng Lyu Wenguang Chen A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules. 2025 ICLR https://openreview.net/forum?id=KnoS9XxIlK conf/iclr/2025 db/conf/iclr/iclr2025.html#LuoWHSL0LC25 Abhishek Panigrahi Nikunj Saunshi Kaifeng Lyu Sobhan Miryoosefi Sashank J. Reddi Satyen Kale Sanjiv Kumar Efficient stagewise pretraining via progressive subnetworks. 2025 ICLR https://openreview.net/forum?id=Y5LjYI4N6P conf/iclr/2025 db/conf/iclr/iclr2025.html#PanigrahiSLMRKK25 Xiangyu Qi Ashwinee Panda Kaifeng Lyu Xiao Ma 0010 Subhrajit Roy Ahmad Beirami Prateek Mittal Peter Henderson 0002 Safety Alignment Should be Made More Than Just a Few Tokens Deep. 2025 ICLR https://openreview.net/forum?id=6Mxhg9PtDE conf/iclr/2025 db/conf/iclr/iclr2025.html#QiPL0RBM025 Kaiyue Wen Xingyu Dang Kaifeng Lyu RNNs are not Transformers (Yet): The Key Bottleneck on In-Context Retrieval. 2025 ICLR https://openreview.net/forum?id=h3wbI8Uk1Z conf/iclr/2025 db/conf/iclr/iclr2025.html#WenDL25 Marko Medvedev Kaifeng Lyu Dingli Yu Sanjeev Arora Zhiyuan Li 0005 Nathan Srebro Weak-to-Strong Generalization Even in Random Feature Networks, Provably. 2025 ICML https://proceedings.mlr.press/v267/medvedev25a.html https://openreview.net/forum?id=OUzDIhgiqr conf/icml/2025 db/conf/icml/icml2025.html#MedvedevLYA0S25

Marko Medvedev Kaifeng Lyu Dingli Yu Sanjeev Arora Zhiyuan Li 0005 Nathan Srebro Weak-to-Strong Generalization Even in Random Feature Networks, Provably. 2025 March abs/2503.02877 CoRR https://doi.org/10.48550/arXiv.2503.02877 db/journals/corr/corr2503.html#abs-2503-02877 streams/journals/corr

Rui Lu 0001 Runzhe Wang Kaifeng Lyu Xitai Jiang Gao Huang 0001 Mengdi Wang 0001 Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias. 2025 March abs/2503.03595 CoRR https://doi.org/10.48550/arXiv.2503.03595 db/journals/corr/corr2503.html#abs-2503-03595 streams/journals/corr

Kairong Luo Haodong Wen Shengding Hu Zhenbo Sun Zhiyuan Liu 0001 Maosong Sun 0001 Kaifeng Lyu Wenguang Chen A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules. 2025 March abs/2503.12811 CoRR https://doi.org/10.48550/arXiv.2503.12811 db/journals/corr/corr2503.html#abs-2503-12811 streams/journals/corr

Kexian Tang Junyao Gao 0002 Yanhong Zeng Haodong Duan Yanan Sun 0005 Zhening Xing Wenran Liu Kaifeng Lyu Kai Chen 0026 LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning? 2025 March abs/2503.19990 CoRR https://doi.org/10.48550/arXiv.2503.19990 db/journals/corr/corr2503.html#abs-2503-19990 streams/journals/corr

Xinran Gu Kaifeng Lyu Jiazheng Li 0015 Jingzhao Zhang Data Mixing Can Induce Phase Transitions in Knowledge Acquisition. 2025 May abs/2505.18091 CoRR https://doi.org/10.48550/arXiv.2505.18091 db/journals/corr/corr2505.html#abs-2505-18091 streams/journals/corr

Jiaxuan Gao Shu Yan Qixin Tan Lu Yang Shusheng Xu Wei Fu Zhiyu Mei Kaifeng Lyu Yi Wu 0013 How Far Are We from Optimal Reasoning Efficiency? 2025 June abs/2506.07104 CoRR https://doi.org/10.48550/arXiv.2506.07104 db/journals/corr/corr2506.html#abs-2506-07104 streams/journals/corr

Marko Medvedev Kaifeng Lyu Zhiyuan Li 0005 Nathan Srebro Shift is Good: Mismatched Data Mixing Improves Test Performance. 2025 October abs/2510.25108 CoRR https://doi.org/10.48550/arXiv.2510.25108 db/journals/corr/corr2510.html#abs-2510-25108 streams/journals/corr

Xinghan Li Haodong Wen Kaifeng Lyu Adam Reduces a Unique Form of Sharpness: Theoretical Insights Near the Minimizer Manifold. 2025 November abs/2511.02773 CoRR https://doi.org/10.48550/arXiv.2511.02773 db/journals/corr/corr2511.html#abs-2511-02773 streams/journals/corr

Shaowen Wang Yiqi Dong Ruinian Chang Tansheng Zhu Yuebo Sun Kaifeng Lyu Jian Li When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs. 2025 November abs/2511.07318 CoRR https://doi.org/10.48550/arXiv.2511.07318 db/journals/corr/corr2511.html#abs-2511-07318 streams/journals/corr

Tingkai Yan Haodong Wen Binghui Li Kairong Luo Wenguang Chen Kaifeng Lyu Larger Datasets Can Be Repeated More: A Theoretical Analysis of Multi-Epoch Scaling in Linear Regression. 2025 November abs/2511.13421 CoRR https://doi.org/10.48550/arXiv.2511.13421 db/journals/corr/corr2511.html#abs-2511-13421 streams/journals/corr

Kairong Luo Zhenbo Sun Haodong Wen Xinyu Shi Jiarui Cui Chenyi Dang Kaifeng Lyu Wenguang Chen How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining. 2025 November abs/2511.18903 CoRR https://doi.org/10.48550/arXiv.2511.18903 db/journals/corr/corr2511.html#abs-2511-18903 streams/journals/corr

Kairong Luo Zhenbo Sun Xinyu Shi Shengqi Chen 0001 Bowen Yu 0003 Yunyi Chen Chenyi Dang Hengtao Tao Hui Wang Fangming Liu Kaifeng Lyu Wenguang Chen PCMind-2.1-Kaiyuan-2B Technical Report. 2025 December abs/2512.07612 CoRR https://doi.org/10.48550/arXiv.2512.07612 db/journals/corr/corr2512.html#abs-2512-07612 streams/journals/corr

Jiachen T. Wang Tong Wu Kaifeng Lyu James Zou 0001 Dawn Song Ruoxi Jia 0001 Prateek Mittal Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice. 2025 December abs/2512.24503 CoRR https://doi.org/10.48550/arXiv.2512.24503 db/journals/corr/corr2512.html#abs-2512-24503 streams/journals/corr

Xinran Gu Kaifeng Lyu Sanjeev Arora Jingzhao Zhang Longbo Huang A Quadratic Synchronization Rule for Distributed Deep Learning. 2024 ICLR https://openreview.net/forum?id=yroyhkhWS6 conf/iclr/2024 db/conf/iclr/iclr2024.html#GuLAZH24 Kaifeng Lyu Jikai Jin Zhiyuan Li 0005 Simon Shaolei Du Jason D. Lee Wei Hu 0014 Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. 2024 ICLR https://openreview.net/forum?id=XsHqr9dEGH conf/iclr/2024 db/conf/iclr/iclr2024.html#LyuJ0DL024 Runzhe Wang Sadhika Malladi Tianhao Wang 0017 Kaifeng Lyu Zhiyuan Li 0005 The Marginal Value of Momentum for Small Learning Rate SGD. 2024 ICLR https://openreview.net/forum?id=3JjJezzVkT conf/iclr/2024 db/conf/iclr/iclr2024.html#WangM0L024 Yongchao Zhou Kaifeng Lyu Ankit Singh Rawat Aditya Krishna Menon Afshin Rostamizadeh Sanjiv Kumar Jean-François Kagy Rishabh Agarwal DistillSpec: Improving Speculative Decoding via Knowledge Distillation. 2024 ICLR https://openreview.net/forum?id=rsY6J3ZaTF conf/iclr/2024 db/conf/iclr/iclr2024.html#ZhouLRMRKKA24 Kaifeng Lyu Haoyu Zhao Xinran Gu Dingli Yu Anirudh Goyal Sanjeev Arora Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. 2024 NeurIPS http://papers.nips.cc/paper_files/paper/2024/hash/d6f034bb216b472fc7d32ec7aff20342-Abstract-Conference.html conf/nips/2024 db/conf/nips/neurips2024.html#LyuZGYGA24

Abhishek Panigrahi Nikunj Saunshi Kaifeng Lyu Sobhan Miryoosefi Sashank J. Reddi Satyen Kale Sanjiv Kumar Efficient Stagewise Pretraining via Progressive Subnetworks. 2024 abs/2402.05913 CoRR https://doi.org/10.48550/arXiv.2402.05913 db/journals/corr/corr2402.html#abs-2402-05913

Kaiyue Wen Xingyu Dang Kaifeng Lyu RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval. 2024 abs/2402.18510 CoRR https://doi.org/10.48550/arXiv.2402.18510 db/journals/corr/corr2402.html#abs-2402-18510

Kaifeng Lyu Haoyu Zhao Xinran Gu Dingli Yu Anirudh Goyal Sanjeev Arora Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. 2024 abs/2402.18540 CoRR https://doi.org/10.48550/arXiv.2402.18540 db/journals/corr/corr2402.html#abs-2402-18540

Xiangyu Qi Ashwinee Panda Kaifeng Lyu Xiao Ma 0010 Subhrajit Roy Ahmad Beirami Prateek Mittal Peter Henderson 0002 Safety Alignment Should Be Made More Than Just a Few Tokens Deep. 2024 abs/2406.05946 CoRR https://doi.org/10.48550/arXiv.2406.05946 db/journals/corr/corr2406.html#abs-2406-05946

Vedant Shah Dingli Yu Kaifeng Lyu Simon Park 0002 Nan Rosemary Ke Michael Mozer Yoshua Bengio Sanjeev Arora Anirudh Goyal AI-Assisted Generation of Difficult Math Questions. 2024 abs/2407.21009 CoRR https://doi.org/10.48550/arXiv.2407.21009 db/journals/corr/corr2407.html#abs-2407-21009 streams/journals/corr

Binghui Li Zhixuan Pan Kaifeng Lyu Jian Li Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks. 2024 abs/2410.10322 CoRR https://doi.org/10.48550/arXiv.2410.10322 db/journals/corr/corr2410.html#abs-2410-10322 streams/journals/corr

Xinran Gu Kaifeng Lyu Longbo Huang Sanjeev Arora Why (and When) does Local SGD Generalize Better than SGD? 2023 ICLR https://openreview.net/forum?id=svCcui6Drl conf/iclr/2023 db/conf/iclr/iclr2023.html#GuLHA23 Jikai Jin Zhiyuan Li 0005 Kaifeng Lyu Simon Shaolei Du Jason D. Lee Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. 15200-15238 2023 ICML https://proceedings.mlr.press/v202/jin23a.html conf/icml/2023 db/conf/icml/icml2023.html#Jin0LDL23

Jikai Jin Zhiyuan Li 0005 Kaifeng Lyu Simon S. Du Jason D. Lee Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. 2023 abs/2301.11500 CoRR https://doi.org/10.48550/arXiv.2301.11500 db/journals/corr/corr2301.html#abs-2301-11500

Xinran Gu Kaifeng Lyu Longbo Huang Sanjeev Arora Why (and When) does Local SGD Generalize Better than SGD? 2023 abs/2303.01215 CoRR https://doi.org/10.48550/arXiv.2303.01215 db/journals/corr/corr2303.html#abs-2303-01215

Runzhe Wang Sadhika Malladi Tianhao Wang 0017 Kaifeng Lyu Zhiyuan Li 0005 The Marginal Value of Momentum for Small Learning Rate SGD. 2023 abs/2307.15196 CoRR https://doi.org/10.48550/arXiv.2307.15196 db/journals/corr/corr2307.html#abs-2307-15196

Yongchao Zhou Kaifeng Lyu Ankit Singh Rawat Aditya Krishna Menon Afshin Rostamizadeh Sanjiv Kumar Jean-François Kagy Rishabh Agarwal DistillSpec: Improving Speculative Decoding via Knowledge Distillation. 2023 abs/2310.08461 CoRR https://doi.org/10.48550/arXiv.2310.08461 db/journals/corr/corr2310.html#abs-2310-08461

Xinran Gu Kaifeng Lyu Sanjeev Arora Jingzhao Zhang Longbo Huang A Quadratic Synchronization Rule for Distributed Deep Learning. 2023 abs/2310.14423 CoRR https://doi.org/10.48550/arXiv.2310.14423 db/journals/corr/corr2310.html#abs-2310-14423

Kaifeng Lyu Jikai Jin Zhiyuan Li 0005 Simon S. Du Jason D. Lee Wei Hu 0014 Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. 2023 abs/2311.18817 CoRR https://doi.org/10.48550/arXiv.2311.18817 db/journals/corr/corr2311.html#abs-2311-18817

Arushi Gupta Nikunj Saunshi Dingli Yu Kaifeng Lyu Sanjeev Arora New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. 2022 conf/nips/2022 NeurIPS http://papers.nips.cc/paper_files/paper/2022/hash/d6383e7643415842b48a5077a1b09c98-Abstract-Conference.html db/conf/nips/neurips2022.html#GuptaSYLA22 Kaifeng Lyu Zhiyuan Li 0005 Sanjeev Arora Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. 2022 conf/nips/2022 NeurIPS http://papers.nips.cc/paper_files/paper/2022/hash/dffd1c523512e557f4e75e8309049213-Abstract-Conference.html db/conf/nips/neurips2022.html#Lyu0A22 Sadhika Malladi Kaifeng Lyu Abhishek Panigrahi Sanjeev Arora On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. 2022 conf/nips/2022 NeurIPS http://papers.nips.cc/paper_files/paper/2022/hash/32ac710102f0620d0f28d5d05a44fe08-Abstract-Conference.html db/conf/nips/neurips2022.html#MalladiLPA22

Sadhika Malladi Kaifeng Lyu Abhishek Panigrahi Sanjeev Arora On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. 2022 abs/2205.10287 CoRR https://doi.org/10.48550/arXiv.2205.10287 db/journals/corr/corr2205.html#abs-2205-10287

Kaifeng Lyu Zhiyuan Li 0005 Sanjeev Arora Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. 2022 abs/2206.07085 CoRR https://doi.org/10.48550/arXiv.2206.07085 db/journals/corr/corr2206.html#abs-2206-07085

Arushi Gupta Nikunj Saunshi Dingli Yu Kaifeng Lyu Sanjeev Arora New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. 2022 abs/2211.02912 CoRR https://doi.org/10.48550/arXiv.2211.02912 db/journals/corr/corr2211.html#abs-2211-02912

Zhiyuan Li 0005 Yuping Luo Kaifeng Lyu Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning. 2021 ICLR https://openreview.net/forum?id=AHOs7Sm5H7R conf/iclr/2021 db/conf/iclr/iclr2021.html#LiLL21 Kaifeng Lyu Zhiyuan Li 0005 Runzhe Wang Sanjeev Arora Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. 12978-12991 2021 NeurIPS https://proceedings.neurips.cc/paper/2021/hash/6c351da15b5e8a743a21ee96a86e25df-Abstract.html conf/nips/2021 db/conf/nips/neurips2021.html#LyuLWA21

Kaifeng Lyu Zhiyuan Li 0005 Runzhe Wang Sanjeev Arora Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias. 2021 abs/2110.13905 CoRR https://arxiv.org/abs/2110.13905 db/journals/corr/corr2110.html#abs-2110-13905

Kaifeng Lyu Jian Li 0015 Gradient Descent Maximizes the Margin of Homogeneous Neural Networks. 2020 ICLR https://openreview.net/forum?id=SJeLIgBKPS conf/iclr/2020 db/conf/iclr/iclr2020.html#LyuL20 Zhiyuan Li 0005 Kaifeng Lyu Sanjeev Arora Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. 2020 NeurIPS https://proceedings.neurips.cc/paper/2020/hash/a7453a5f026fb6831d68bdc9cb0edcae-Abstract.html conf/nips/2020 db/conf/nips/neurips2020.html#0005LA20

Zhiyuan Li 0005 Kaifeng Lyu Sanjeev Arora Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. 2020 abs/2010.02916 CoRR https://arxiv.org/abs/2010.02916 db/journals/corr/corr2010.html#abs-2010-02916

Zhiyuan Li 0005 Yuping Luo Kaifeng Lyu Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning. 2020 abs/2012.09839 CoRR https://arxiv.org/abs/2012.09839 db/journals/corr/corr2012.html#abs-2012-09839

Sanjeev Arora Zhiyuan Li 0005 Kaifeng Lyu Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. 2019 ICLR (Poster) https://openreview.net/forum?id=rkxQ-nA9FX conf/iclr/2019 db/conf/iclr/iclr2019.html#AroraLL19 Lijie Chen 0001 Shafi Goldwasser Kaifeng Lyu Guy N. Rothblum Aviad Rubinstein Fine-grained Complexity Meets IP = PSPACE. 1-20 2019 SODA https://doi.org/10.1137/1.9781611975482.1 https://dl.acm.org/citation.cfm?id=3310436 conf/soda/2019 db/conf/soda/soda2019.html#ChenGLRR19

Kaifeng Lyu Jian Li 0015 Gradient Descent Maximizes the Margin of Homogeneous Neural Networks. 2019 abs/1906.05890 CoRR http://arxiv.org/abs/1906.05890 db/journals/corr/corr1906.html#abs-1906-05890

Ran Duan Kaifeng Lyu Yuanhang Xie Single-Source Bottleneck Path Algorithm Faster than Sorting for Sparse Graphs. 43:1-43:14 2018 ICALP https://doi.org/10.4230/LIPIcs.ICALP.2018.43 conf/icalp/2018 db/conf/icalp/icalp2018.html#DuanLX18

Lijie Chen 0001 Shafi Goldwasser Kaifeng Lyu Guy N. Rothblum Aviad Rubinstein Fine-grained Complexity Meets IP = PSPACE. 2018 abs/1805.02351 CoRR http://arxiv.org/abs/1805.02351 db/journals/corr/corr1805.html#abs-1805-02351

Ran Duan Kaifeng Lyu Hongxun Wu Yuanhang Xie Single-Source Bottleneck Path Algorithm Faster than Sorting for Sparse Graphs. 2018 abs/1808.10658 CoRR http://arxiv.org/abs/1808.10658 db/journals/corr/corr1808.html#abs-1808-10658

Sanjeev Arora Zhiyuan Li 0005 Kaifeng Lyu Theoretical Analysis of Auto Rate-Tuning by Batch Normalization. 2018 abs/1812.03981 CoRR http://arxiv.org/abs/1812.03981 db/journals/corr/corr1812.html#abs-1812-03981

Rishabh Agarwal Sanjeev Arora Ahmad Beirami Yoshua Bengio Ruinian Chang Kai Chen 0026 Lijie Chen 0001 Shengqi Chen 0001 Wenguang Chen Yunyi Chen Jia-Rui CuiJiarui Cui Chenyi Dang Xingyu Dang Yiqi Dong Simon S. DuSimon Shaolei Du Haodong Duan Ran Duan Wei Fu Jiaxuan Gao Junyao Gao 0002 Shafi Goldwasser Anirudh Goyal Xinran Gu Arushi Gupta Peter Henderson 0002 Shengding Hu Wei Hu 0014 Gao Huang 0001 Longbo Huang Ruoxi Jia 0001 Xitai Jiang Jikai Jin Jean-François Kagy Satyen Kale Nan Rosemary Ke Sanjiv Kumar Jason D. Lee Binghui Li He Li Jian Li Jian Li 0015 Jiazheng Li 0015 Xinghan Li Zhiyuan Li 0005 Fangming Liu Wenran Liu Zhiyuan Liu 0001 Rui Lu 0001 Kairong Luo Yuping Luo Xiao Ma 0010 Sadhika Malladi Marko Medvedev Zhiyu Mei Aditya Krishna Menon Sobhan Miryoosefi Prateek Mittal Michael C. MozerMichael Mozer Zhixuan Pan Ashwinee Panda Abhishek Panigrahi Simon Park 0002 Xiangyu Qi Ankit Singh Rawat Sashank J. Reddi Afshin Rostamizadeh Guy N. Rothblum Subhrajit Roy Aviad Rubinstein Nikunj Saunshi Vedant Shah Xinyu Shi Dawn Song Nathan Srebro Maosong Sun 0001 Yanan Sun 0005 Yuebo Sun Zhenbo Sun Qixin Tan Kexian Tang Hengtao Tao Hui Wang Jiachen T. Wang Jiani Wang Mengdi Wang 0001 Runzhe Wang Shaowen Wang Tianhao Wang 0017 Xiaohan Wang Haodong Wen Kaiyue Wen Hongxun Wu Tong Wu Yi Wu 0013 Yuanhang Xie Zhening Xing Shusheng Xu Shu Yan Tingkai Yan Lu Yang Serena Yeung-Levy Bowen Yu 0003 Dingli Yu Yanhong Zeng Jingzhao Zhang Yuhui Zhang Haoyu Zhao Yongchao Zhou Tansheng Zhu James Zou 0001