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Student Awards:
- (May 2026) Shenyuan, Wenqiang, Zijian passed their thesis exams. Congratulations, Dr. Gao, Dr. Sun, and Dr. Li!
- (May 2026) Zifan received the HKUST RedBird Academic Excellence Award for Continuing PhD Students.
- (April 2026) Qirui and Xizhi received the Hong Kong PhD Fellowship. Congratulations!
- (February 2026) Xinran passed her thesis exam. Congratulations, Dr. Li!
- (August 2025) Jiawei received the IEEE Communications Society Katherine Johnson Young Author Best Paper Award for paper “Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach”. [Paper] [Award Information]
- (August 2025) Zhening received The ECE Best TA Award 2024/25.
Call for Papers
- IEEE Journal on Selected Topics in Signal Processing, special issue on “Wireless Foundation Models for AI-native 6G and Beyond,” deadline: May 15, 2026. [Call for Papers]
- Special Collection on “AI-Driven Wireless Channel Modeling and Prediction,” npj Wireless Technology, submission deadline: 30 June 2026. [Website]
Recent Research Results
- (WirelessBench) “WirelessBench: A Tolerance-Aware LLM Agent Benchmark for Wireless Network Intelligence,” preprint. [Paper] [Project Page]
- (WirelessAgent++) “WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks,” preprint. [Paper]
- (URF-GS) “Bridging Visual and Wireless Sensing: A Unified Radiation Field for 3D Radio Map Construction,” preprint. [Paper]
- (PrismMirror) “Real-Time Human Frontal View Synthesis from a Single Image,” preprint. [Paper]
- (Mon3tr) “Mon3tr: Monocular 3D Telepresence with Pre-built Gaussian Avatars as Amortization,” preprint. [Paper]
(ICML 26) 4 papers accepted by ICML 2026.
(MobileROS) “MobileROS: A Wireless-Native Robot Operating System for Mobile Robotics,” IEEE Transactions on Robotics, to appear.
(ACL 26) “Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing,” accepted by ACL 26. [Paper]
(CVPR 26) 4 papers accepted by CVPR 2026.
(ICLR 26) 9 papers accepted by ICLR 2026.
(npj Wireless Technology) “Towards Reasoning-Empowered Task-Oriented Communication for Agent Networks,” accepted by npj Wireless Technology.
(npj Artificial Intelligence) “Learning design-score manifold to guide diffusion models for offline optimization,” published by npj Artificial Intelligence. [Paper]
(Nature Communications) Paper “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher” was published by Nature Communications, 2024. [Paper]

Research Interests

At iComAI Lab, we pursue cutting-edge research at the intersection of communications technology and AI, with a particular focus on world understanding and intelligent decision making. We are deeply passionate about advancing the frontiers of two rapidly evolving fields:

World Models: developing computational models that enable machines to build internal representations of their environments, predict future states, and support planning, simulation, and adaptive interaction in the physical and digital worlds.
AI Agents: exploring the design and deployment of intelligent agents—ranging from virtual and wearable agents to robotic systems—that can perceive, reason, and act autonomously in complex environments;

Together, these two areas form the foundation of our vision: building intelligent systems that not only understand the world—but bring it to life.

Selected Publications

World Models

S. Gao, et. al, “DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos,” International Conference on Machine Learning (ICML), Seoul, South Korea, July 2026. (Spotlight: 2.2%) [Paper] [Project Page]
W. Sun, H. Zhang, H. Wang, J. Wu, Z. Wang, Z. Wang, Y. Wang, J. Zhang, T. Wang, and C. Guo, “WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling,” International Conference on Machine Learning (ICML), Seoul, South Korea, July 2026. [Paper] [Demo]
S. Gao, S. Zhou, Y. Du, J. Zhang, and C. Gan, “AdaWorld: Learning adaptable world models with latent actions,” International Conference on Machine Learning (ICML), Vancouver, Canada, July 2025. [Project Page]
W. Sun, S. Chen, F. Liu, Z. Chen, Y. Duan, J. Zhang, and Y. Wang, “DimensionX: Create any 3D and 4D scenes from a single image with decoupled video diffusion,” International Conference on Computer Vision (ICCV), Honolulu, Hawai'i, USA, Oct. 2025. [Paper] [Code] [Project Page]
S. Gao, J. Yang, L. Chen, K. Chitta, Y. Qiu, A. Geiger, J. Zhang, and H. Li, “Vista: A generalizable driving world model with high fidelity and versatile controllability,” The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2024. [Paper] [Code] [Demo]

AI Agent, LLM/MLLM Reasonging

S. Li, Y. Huang, Z. Liu, Z. Li, J. fu, L. Song, J. Bian, J. Zhang, and R. Wang, “Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory,” International Conference on Machine Learning (ICML), Seoul, South Korea, July 2026. [Paper]
X. Li, G. Huzhang, S. Shen, Q.-G. Chen, Z. Xu, W. Luo, K. Zhang, and J. Zhang, “Getting Your LLMs Ready for Reinforcement Learning with Lightweight SFT,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [Paper] [Video]
S. Zhang, Z. Li, Y. Zhang, J. Fu, L. Song, J. Bian, J. Zhang, Y. Yang, and R. Wang, “PixelCraft: A multi-agent system for high-fidelity visual reasoning on structured images,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [Paper] [GitHub]
Z. Li, X. Guan, B. Zhang, S. Huang, H. Zhou, S. Lai, M. Yan, Y. Jiang, P. Xie, F. Huang, J. Zhang, and J. Zhou, “WebWeaver: Structuring web-scale evidence with dynamic outlines for open-ended deep research,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [Paper]

3D/4D Representation, Reconstruction, Compression

F. Lin, Y. Hu, L. Zhu, Z. Liu, Y. Zhuang, Z. Lin, and J. Zhang, “Real-Time Human Frontal View Synthesis from a Single Image,” preprint. [Paper]
F. Lin, Y. Hu, Z. Liu, Y. Zhuang, Z. Lin, and J. Zhang, “Mon3tr: Monocular 3D Telepresence with Pre-built Gaussian Avatars as Amortization,” preprint. [Paper]
Z. Liu, R. Song, Y. Huang, Y. Hu, X. Zhang, J. Shao, Z. Lin, and Jun Zhang, “Feed-forward 3D Gaussian splatting compression with long-context modeling,” preprint. [Paper]
J. Bao, H. Chen, L. Zhu, C. Liu, R. Zhang, K. Luo, Z. Hu, W. Chen, Y. Yin, X. Wang, Z. Lin, J. Zhang, and X. Han, “LumiTex: Towards high-fidelity PBR texture generation with illumination context,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [Paper]
X. Zhang, Z. Liu, Y. Zhang, X. Ge, D. He, T. Xu, Y. Wang, Z. Lin, S. Yan, and J. Zhang, “MEGA: Memory-efficient 4D Gaussian splatting for dynamic scenes,” International Conference on Computer Vision (ICCV), Honolulu, Hawai'i, USA, Oct. 2025. (Highlight) [Paper]
H. Chen, Z. Lin, and J. Zhang, “GI-GS: Global illumination decomposition on Gaussian splatting for inverse rendering,” International Conference on Learning Representations (ICLR), Singapore, April 2025. [Paper] [Project Page]

Efficient and Safe AI

L. Zhu, Y. Huang, X. Ge, Y. Xue, Z. Liu, Y. Zhang, Z. Lin, and J. Zhang, “Flash-VAED: Plug-and-Play VAE Decoders for Efficient Video Generation,” International Conference on Machine Learning (ICML), Seoul, South Korea, July 2026. [Paper]
L. Long, Y. Huang, S. Bai, R. Gong, J. Zhang, A. Zhou, and J. Yang, “Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing,” The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), San Diego, California, USA, July 2026. (Main paper, Acceptance Rate: 19%) [Paper]
Y. Huang, Z. Wang, Z. Yuan, Y. Ding, R. Gong, J. Guo, X. Liu, and J. Zhang, “MoDES: Accelerating mixture-of-experts multimodal large language models via dynamic expert skipping,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Denver, Colorado, USA, June 2026. [Paper]
Y. Huang, X. Ge, R. Gong, C. Lv, and J. Zhang, “LinVideo: A Post-training framework towards O(n) attention in efficient video generation,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Denver, Colorado, USA, June 2026. (Highlight) [Paper]
Y. Li, Z. Liu, Z. Li, Z. Lin, and J. Zhang, “RemedyGS: Defend 3D Gaussian Splatting Against Computation Cost Attacks,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Denver, Colorado, USA, June 2026. [Paper]
Y. Li, Z. Liu, Z. Li, Z. Lin, and J. Zhang, “Token-level Data Selection for Safe LLM Fine-tuning,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [Paper]
Y. Huang, R. Gong, J. Liu, Y. Ding, C. Lv, H. Qin, and J. Zhang, “QVGen: Pushing the limit of quantized video generative models,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [Paper]
X. Ge, X. Zhang, T. Xu, Y. Zhang, X. Zhang, Y. Wang, and J. Zhang, “SenseFlow: Scaling distribution matching for flow-based text-to-image distillation,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [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]

Reinforcement Learning

Z. Liu, X. Li, S. Chen, and J. Zhang, “GAS: Enhancing Reward-Cost Balance of Generative Model-assisted Offline Safe RL,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [Paper]
Z. Liu, X. Li, and J. Zhang, “C2IQL: Constraint-conditioned implicit Q-learning for safe offline reinforcement learning,” International Conference on Machine Learning (ICML), Vancouver, Canada, July 2025. [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. [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. [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. [Paper] [Code] [Video]

Neural Data Representation and Compression

S. Qin, X. Zhang, Z. Liu, J. Wang, B. Chen, J. Li, Y. Ren, S.-T. Xia, and J. Zhang, “MambaSIC: Mamba-based Stereo Image Compression with Bi-directional Multi-reference Entropy Model,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Denver, Colorado, USA, June 2026. [Paper]
R. Song, Y. Wang, T. Xu, Z. Liu, Z. Lin, and J. Zhang, “Low-Latency Neural LiDAR Compression with 2D Context Models,” International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April 2026. [Paper] [Code]
X. Zhang*, X. Ge*, T. Xu, D. He, Y. Wang, H. Qin, G. Lu, J. Geng, and J. Zhang, “GaussianImage: 1000 FPS image representation and compression by 2D Gaussian splatting,” European Conference on Computer Vision (ECCV), Milano, Italy, Sept.-Oct. 2024. (* equal contribution) [Paper] [Code]
X. Zhang, R. Yang, D. He, X. Ge, T. Xu, Y. Wang, H. Qin, and J. Zhang, “Boosting neural representations for videos with a conditional decoder,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, Jun. 2024. (Highlight, Top 2.8%) [Paper] [Code]
X. Ge, J. Luo, X. Zhang, T. Xu, G. Lu, D. He, J. Geng, Y. Wang, J. Zhang, and H. Qin, “Task-aware encoder control for deep video compression,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, Jun. 2024. [Paper]

O-RAN, Edge AI

P. Jiang, F. Liu, J. Guo, C.-K. Wen, S. Jin, J. Zhang, “Intention-Aware Semantic Agent Communications for AI Glasses,” preprint. [Paper]
B. Liu, Q. Zhang, Y. Lu, J. Jiao, J. Chauhan, W. Wu, J. Zhang, and D. Kanoulas, “MobileROS: A Wireless-Native Robot Operating System for Mobile Robotics,” IEEE Transactions on Robotics, to appear.
B. Liu, Y. Lu, J. Zhao, Q. Yang, W. Wu, L. Chen, J. Chauhan, and J. Zhang, “WiLLM: an open framework for LLM services over wireless systems,” preprint. [Paper]
Y. Zhuang, Z. Meng, Z. Lin, and J. Zhang, “OCC: Physical-Layer Assisted Congestion Control for Real-Time Communications,” preprint. [Paper]
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]
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. (The 2025 IEEE Communications Society Katherine Johnson Young Author Best Paper Award) [Paper] [GitHub]

AI for Communications

J. Tong, F. Liu, L. Xv, S. Lu, K. Li, Y. Zhang, Y. Song, Z. Xue, and J. Zhang, “WirelessBench: A Tolerance-Aware LLM Agent Benchmark for Wireless Network Intelligence,” preprint. [Paper] [Project Page]
J. Tong, Z. Li, F. Liu, W. Guo, and J. Zhang, “WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks,” preprint. [Paper]
C. Wen, J. Tong, Z. Lin, C. Bian, and J. Zhang, “Bridging Visual and Wireless Sensing: A Unified Radiation Field for 3D Radio Map Construction,” preprint. [Paper]
S. Xie, H. Li, Z. Wang, S.H. Song, J. Zhang, and K. B. Letaief, “Towards Reasoning-Empowered Task-Oriented Communication for Agent Networks,” npj Wireless Technology, to appear.
C. Wen, J. Tong, Z. Lin, and J. Zhang, “Neural representation for wireless radiation field reconstruction: A 3D Gaussian splatting approach,” IEEE Trans. Wireless Commun., to appear. [Paper]
J. Tong, J. Shao, Q. Wu, W. Guo, Z. Li, Z. Lin, and J. Zhang, “WirelessAgent: Large language model agents for intelligent wireless networks,” China Communications, vol. 23, no. 3, pp. 265-285, March 2026. [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]

Jun Zhang

What's new

Research Interests

Selected Publications