I am a PhD student in Computer Science at the National University of Singapore and Co-Founder at Evolvent AI. My research focuses on multimodal reasoning, reinforcement learning, and self-evolving agents.
I am interested in building AI systems that improve through data, feedback, and experience. My work spans multimodal benchmarks, rule-based RL, model evaluation, and scalable data engines. Previously, I conducted research at Kimi and Shanghai AI Laboratory.

Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, Ping Luo, Yu Qiao, Qiaosheng Zhang, Wenqi Shao
Technical Report 2025
Extends large-scale rule-based reinforcement learning to multimodal reasoning and releases the full models, data, and training pipeline.
Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, Ping Luo, Yu Qiao, Qiaosheng Zhang, Wenqi Shao
Technical Report 2025
Extends large-scale rule-based reinforcement learning to multimodal reasoning and releases the full models, data, and training pipeline.

Fanqing Meng*, Jiaqi Liao*, Xinyu Tan, Quanfeng Lu, Wenqi Shao, Kaipeng Zhang, Yu Cheng, Dianqi Li, Ping Luo (* equal contribution)
International Conference on Machine Learning (ICML) 2025
Introduces PhyGenBench to evaluate whether generated videos obey physical commonsense across diverse real-world scenarios.
Fanqing Meng*, Jiaqi Liao*, Xinyu Tan, Quanfeng Lu, Wenqi Shao, Kaipeng Zhang, Yu Cheng, Dianqi Li, Ping Luo (* equal contribution)
International Conference on Machine Learning (ICML) 2025
Introduces PhyGenBench to evaluate whether generated videos obey physical commonsense across diverse real-world scenarios.

Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, Ping Luo
Findings of the Association for Computational Linguistics (ACL) 2024
A chart-focused vision-language model trained through chart-to-table alignment and broad multitask instruction tuning.
Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, Ping Luo
Findings of the Association for Computational Linguistics (ACL) 2024
A chart-focused vision-language model trained through chart-to-table alignment and broad multitask instruction tuning.

Kaining Ying*, Fanqing Meng*, Jin Wang, Zhiqian Li, Han Lin, Yue Yang, Hao Zhang, Wenbo Zhang, Yuqi Lin, Shuo Liu, et al. (* equal contribution)
International Conference on Machine Learning (ICML) 2024
A 31K-question benchmark spanning 162 subtasks for diagnosing broad multimodal model capabilities.
Kaining Ying*, Fanqing Meng*, Jin Wang, Zhiqian Li, Han Lin, Yue Yang, Hao Zhang, Wenbo Zhang, Yuqi Lin, Shuo Liu, et al. (* equal contribution)
International Conference on Machine Learning (ICML) 2024
A 31K-question benchmark spanning 162 subtasks for diagnosing broad multimodal model capabilities.

Fanqing Meng, Wenqi Shao, Zhanglin Peng, Chonghe Jiang, Kaipeng Zhang, Yu Qiao, Ping Luo
Advances in Neural Information Processing Systems (NeurIPS) 2023
Predicts transfer performance across heterogeneous multimodal tasks without brute-force fine-tuning.
Fanqing Meng, Wenqi Shao, Zhanglin Peng, Chonghe Jiang, Kaipeng Zhang, Yu Qiao, Ping Luo
Advances in Neural Information Processing Systems (NeurIPS) 2023
Predicts transfer performance across heterogeneous multimodal tasks without brute-force fine-tuning.