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What's new
(AdaWorld) “AdaWorld: Learning adaptable world models with latent actions,” preprint. [Project Page]
(ICLR 25) Two papers accepted by ICLR 2025.
“GI-GS: Global illumination decomposition on Gaussian splatting for inverse rendering” [Paper] [Project Page]
“Exponential topology-enabled scalable communication in multi-agent reinforcement learning” [Paper]
(NAACL 25) “Graph neural network enhanced retrieval for question answering of large language models,” accepted by NAACL 2025. [Paper]
(AISTATS 25) “Reinforcement learning with intrinsically motivated feedback graph for lost-sales inventory control,” accepted by AISTATS 2025. [Paper]
(INFOCOM 25) “WRF-GS: Wireless radiation field reconstruction with 3D Gaussian splatting,” accepted by INFOCOM 2025. [Paper] [Code]
(AAAI 25) Two papers accepted by AAAI 2025.
(NeurIPS 24) Two papers accepted to NeurIPS 2024.
“Vista: A generalizable driving world model with high fidelity and versatile controllability” [Paper] [Code] [Demo]
“Kaleidoscope: Learnable masks for heterogeneous multi-agent reinforcement learning” [Paper] [Code]
Research Interests
At ComAI Lab, we pursue cutting-edge research at the intersection of communications technology and AI, with a particular focus on intelligent decision making and spatial perception. Our work bridges theoretical foundations with practical applications, addressing fundamental challenges in:
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Group WeChat Account of ComAI Lab (in Chineses)
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Selected Publications
Reinforcement Learning
Z. Liu, X. Li. S. Chen, G. Li, J. Jiang, and J. Zhang, “Reinforcement learning with intrinsically motivated feedback graph for lost-sales inventory control,” International Conference on Artificial Intelligence and Statistics (AISTATS), Mai Khao, Thailand, May 2025. (Acceptance Rate: 31.3%) [Paper]
X. Li, X. Wang, C. Bai, and J. Zhang, “Exponential topology-enabled scalable communication in multi-agent reinforcement learning,” International Conference on Learning Representations (ICLR), Singapore, April 2025. (Acceptance Rate: 32.08%) [Paper]
X. Li, L. Pan, and J. Zhang, “Kaleidoscope: Learnable masks for heterogeneous multi-agent reinforcement learning,” The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2024. (Acceptance Rate: 25.8%) [Paper] [Code]
X. Li, Z. Liu, S. Chen, and J. Zhang, “Individual contributions as intrinsic exploration scaffolds for multi-agent reinforcement learning,” International Conference on Machine Learning (ICML), Vienna, Austria, July 2024. (Acceptance Rate: 27.5%) [Paper] [Code] [Video]
Safe and Efficient AI
Y. Huang, Z. Wang, R. Gong, J. Liu, X. Zhang, J. Guo, X. Liu, and J. Zhang, “HarmoniCa: Harmonizing training and inference for better feature cache in diffusion transformer acceleration,” preprint. [Paper]
Y. Sun, X. Li, T. Lin, and J. Zhang, “Learn how to query from unlabeled data streams in federated learning,” Pro. AAAI Conference on Artificial Intelligence, Philadelphia, Pennsylvania, USA, Feb.-Mar. 2025. (Acceptance Rate: 23.4%) [Paper]
J. Shao, F. Wu, and J. Zhang, “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher,” Nature Communications, vol. 15, no. 349, Jan 2024. [Paper]
B. Li*, Y. Shen*, J. Yang, Y. Wang, J. Ren, T. Che, J. Zhang, and Z. Liu, “Sparse Mixture-of-Experts are Domain Generalizable Learners,” International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. (Oral presentation) [Paper] [GitHub]
J. Shao, Y. Sun, S. Li, and J. Zhang, “DReS-FL: Dropout-resilient secure federated learning for non-IID clients via secret data sharing,” NeurIPS 2022. [Paper] (Acceptance Rate: 25.6%)
O-RAN, Edge AI
B. Liu, J. Tong, and J. Zhang, “LLM-Slice: Dedicated wireless network slicing for large language models,” The 22nd ACM Conference on Embedded Networked Sensor Systems - Posters and Demos, Hangzhou, China, Nov. 2024. [Paper]
Y. Mao, X. Yu, K. Huang, A.-Y. Zhang, and J. Zhang, “Green edge AI: A contemporary survey,” Proc. IEEE, vol. 112, no. 7, pp. 880-911, July 2024. [Paper]
J. Shao, X. Zhang, and J. Zhang, “Task-oriented communication for edge video analytics,” IEEE Trans. Wireless Commun., vol. 23, no. 5, pp. 4141-4154, May 2024. [Paper] [GitHub]
J. Shao, Y. Mao, and J. Zhang, “Task-oriented communication for multidevice cooperative edge inference,” IEEE Trans. Wireless Commun., vol. 11, no. 1, pp. 73-87, Jan. 2023. [Paper]
J. Shao, Y. Mao, and J. Zhang, “Learning task-oriented communication for edge inference: An information bottleneck approach,” IEEE J. Select. Areas Commun, vol. 40, no. 1, pp. 197-211, Jan. 2022. [Paper] [GitHub]
AI for Communications
W. Yu, H. He, S. Song, J. Zhang, L. Dai, L. Zheng, and K. B. Letaief, “AI and deep learning for THz ultra-massive MIMO: From model-driven approaches to foundation models,” preprint. [Paper]
C. Wen, J. Tong, Z. Lin, and J. Zhang, “WRF-GS: Wireless radiation field reconstruction with 3D Gaussian splatting,” Proc. IEEE INFOCOM, London, United Kingdom, May 2025. (Acceptance Rate: 18.7%) [Paper] [Code]
J. Tong, J. Shao, Q. Wu, W. Guo, Z. Li, Z. Lin, and J. Zhang, “WirelessAgent: Large language model agents for intelligent wireless networks,” submitted. [Paper]
J. Shao, J. Tong, Q. Wu, W. Guo, Z. Li, Z. Lin, and Jun Zhang, “WirelessLLM: Empowering large language models towards wireless intelligence,” Journal of Communications and Information Networks, vol. 9, no. 2, pp. 99-112, June 2024. [Paper]
W. Yu, Y. Shen, H. He, X. Yu, S.H. Song, J. Zhang, and K. B. Letaief, “An adaptive and robust deep learning framework for THz ultra-massive MIMO channel estimation,” IEEE J. Sel. Topics Signal Process., vol. 17, no. 4, pp. 761-776, July 2023. [Paper] [GitHub]
Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: architecture design and theoretical analysis,” IEEE J. Select. Areas Commun, vol. 39, no. 1, pp. 101–115, Jan. 2021. [Paper]
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