Publications

2026

1. NeurIPS

   

   LMM-Track4D: Eliciting 4D Dynamic Reasoning in LMMs via Trajectory-Grounded Dialogue

   Chaoyue Li^*, Yongxue Xu^*, Jie Feng^*, and Jiayu Ding^†

   arXiv preprint

   Submitted to NeurIPS 2026

   A trajectory-grounded dialogue benchmark and LMM framework for 4D dynamic reasoning, combining RTGE, a streaming TRK state token, and an OSK-RA decoder for stable 3D trajectory estimation under occlusion and viewpoint variation.

   arXiv Bib Code

   @article{li2026lmmtrack4d,
     title={LMM-Track4D: Eliciting 4D Dynamic Reasoning in LMMs via Trajectory-Grounded Dialogue},
     author={Li, Chaoyue and Xu, Yongxue and Feng, Jie and Ding, Jiayu},
     journal={arXiv preprint arXiv:2605.19390},
     note={Equal contribution: Chaoyue Li, Yongxue Xu, and Jie Feng. Corresponding author: Jiayu Ding},
     year={2026}
   }

2. TNSE

   

   Solving the Robust Influence Maximization Problem in Competitive Multilayer Networks via a Diffusion-Aware Role-Guided Evolutionary Approach

   Yongxue Xu, Ziqian Liu, Yongqing Huang, and Shuai Wang^†

   IEEE Transactions on Network Science and Engineering

   Under review

   A diffusion-aware role-guided evolutionary framework for robust competitive influence maximization on heterogeneous multilayer networks.

   Bib

   @article{xu2026influence,
     title={Solving the Robust Influence Maximization Problem in Competitive Multilayer Networks via a Diffusion-Aware Role-Guided Evolutionary Approach},
     author={Xu, Yongxue and Liu, Ziqian and Huang, Yongqing and Wang, Shuai},
     journal={Under review at IEEE Transactions on Network Science and Engineering},
     note={Corresponding author: Shuai Wang},
     year={2026}
   }

2025

1. GBCESC

   

   Identifying Critical Nodes with Deep Learning and Reinforcement Learning: A Case Study on Urban Road Networks

   Zhonghan Liu^*, Yongxue Xu^*, Wenshuo Liu^*, Ziqian Liu, Yichen Liu, and Shuai Wang^†

   GBCESC 2025

   Best Paper Award

   A benchmark of deep learning and reinforcement learning methods for critical node identification on real urban road networks.

   Bib

   @inproceedings{liu2025criticalnodes,
     title={Identifying Critical Nodes with Deep Learning and Reinforcement Learning: A Case Study on Urban Road Networks},
     author={Liu, Zhonghan and Xu, Yongxue and Liu, Wenshuo and Liu, Ziqian and Liu, Yichen and Wang, Shuai},
     booktitle={GBCESC},
     note={Best Paper Award. Equal contribution: Zhonghan Liu, Yongxue Xu, and Wenshuo Liu. Corresponding author: Shuai Wang},
     year={2025}
   }