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Opponent-aware Order Pricing towards Hub-oriented Mobility Services
Published in 39th IEEE International Conference on Data Engineering (ICDE 2023), 2023
This paper proposes a novel opponent-aware pricing framework for emerging hub-oriented mobility services, significantly improving platform revenue and operational efficiency.
Recommended citation: Zuohan Wu, Libin Zheng, Chen Jason Zhang, Huaijie Zhu, Jian Yin, Di Jiang. "Opponent-aware Order Pricing towards Hub-oriented Mobility Services." ICDE 2023, pp. 1874-1886. https://doi.org/10.1109/ICDE55515.2023.00146
VPLight: A Reinforcement Learning Approach for Traffic Signal Control with Pedestrian Dynamics
Published in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025
This paper proposes VPLight, a reinforcement learning approach that considers pedestrian dynamics for more effective traffic signal control at intersections.
Recommended citation: Xinyu Zhang, Zuohan Wu, Chen Jason Zhang, Libin Zheng, Peng Cheng, Jian Yin, Cyrus Shahabi. "VPLight: A Reinforcement Learning Approach for Traffic Signal Control with Pedestrian Dynamics." IEEE Transactions on Knowledge and Data Engineering, 2025. https://doi.org/10.1109/TKDE.2025.3641213
DRLPG: Reinforced Opponent-Aware Order Pricing for Hub Mobility Services
Published in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025
This paper develops a deep reinforcement learning model with a multi-policy fusion mechanism that enables more adaptive and profitable pricing strategies in dynamic competitive environments.
Recommended citation: Zuohan Wu, Chen Jason Zhang, Han Yin, Rui Meng, Libin Zheng, Huaijie Zhu, Wei Liu. "DRLPG: Reinforced Opponent-Aware Order Pricing for Hub Mobility Services." IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 6, pp. 3298-3311, 2025. https://doi.org/10.1109/TKDE.2025.3551147
Probing the ‘Psyche’ of Large Reasoning Models: Understanding Through a Human Lens
Published in arXiv preprint, 2025
This paper introduces a comprehensive taxonomy to characterize atomic reasoning steps in large reasoning models, grounding their understanding in human cognitive processes.
Recommended citation: Yuxiang Chen, Zuohan Wu, Ziwei Wang, Xiangning Yu, Xujia Li, Linyi Yang, Mengyue Yang, Jun Wang, Lei Chen. "Probing the Psyche of Large Reasoning Models: Understanding Through a Human Lens." arXiv preprint arXiv:2512.00729, 2025. https://arxiv.org/abs/2512.00729
DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation
Published in The Web Conference 2026 (WWW 2026), 2026
This paper proposes DA-RAG, which leverages attributed community search to dynamically extract relevant subgraphs for retrieval-augmented generation, outperforming existing RAG methods by up to 40%.
Recommended citation: Xingyuan Zeng, Zuohan Wu, Yue Wang, Chen Zhang, Quanming Yao, Libin Zheng, Jian Yin. "DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation." WWW 2026.
Efficient Zero-shot and Label-free Log Anomaly Detection for Resource-constrained Systems
Published in 42nd IEEE International Conference on Data Engineering (ICDE 2026), 2026
This paper presents MaidLog, an LLM-based framework achieving industry-leading zero-shot accuracy for log anomaly detection while efficiently running on resource-constrained edge devices.
Recommended citation: Zuohan Wu, Jiachuan Wang, Libin Zheng, Yongqi Zhang, Shuangyin Li, Lei Chen. "Efficient Zero-shot and Label-free Log Anomaly Detection for Resource-constrained Systems." ICDE 2026.