AI4NextG @ NeurIPS'25

About the Workshop

The field of wireless communications and networking is undergoing a paradigm shift, driven by the growing potential of Artificial Intelligence (AI) and Machine Learning (ML) to redefine traditional system design principles. This workshop aims to catalyze interest and foster collaboration between the AI/ML and wireless communications communities. The timing of this workshop is especially significant, as the next-generation (NextG) wireless standardization efforts (such as 6G and WiFi 9) are just getting started, with AI-native technologies expected to play a central role across all aspects of the wireless ecosystem – from radio access to network management and edge intelligence. NextG represents a foundational shift in global infrastructure, enabling ultra-fast, low-latency, and intelligent connectivity that will power future applications in AI, robotics, immersive environments, and autonomous systems. These technologies offer unprecedented opportunities to both drive and benefit many applications, from healthcare and transportation to industrial automation and environmental monitoring. The economic and societal implications are vast: NextG networks will underlie trillions in global GDP impact, bridge digital divides, and shape how billions of people interact with technology and each other in the decades to come.

Despite the clear promise, a significant disconnect exists between the AI/ML and wireless research communities. AI/ML experts often lack an understanding of the unique physical, algorithmic, and architectural constraints inherent in wireless systems, while wireless researchers tend to adopt generic, off-the-shelf AI/ML models that are not optimized for the intricacies of wireless environments. Wireless environments are inherently dynamic, high-dimensional, and partially observable. These unique characteristics make them ideal testbeds for developing robust learning algorithms, particularly in areas like online learning, reinforcement learning, and in-context learning. At the same time, AI/ML techniques are becoming essential for managing the growing complexity of modern wireless networks, including resource allocation, interference mitigation, and cross-layer optimization. Bridging the gap between the two communities is not only necessary for meaningful technological advances but also critical for realizing the full societal impact of intelligent wireless systems.

This workshop aims to bring together researchers and practitioners at the intersection of artificial intelligence (AI), machine learning (ML), and wireless to address the unique challenges andopportunities posed by Next-Generation (NextG) wireless systems. As the 6G era begins to take shape, AI-native designs have emerged as a cornerstone of wireless innovation, with the potential to transform the performance, efficiency, and adaptability of communication systems. The integration of AI/ML is poised to influence every layer of the network stack, from physical-layer signal processing to network control and resource management.

Call for Papers

The workshop will cover a broad range of topics that highlight both foundational research and practical applications of AI/ML in wireless systems. Key themes include, but are not limited to:

• AI-native protocol and architecture design for 6G and WiFi 8/9
• Reinforcement learning for dynamic spectrum access and resource allocation
• Gen AI and foundation models for physical-layer communication tasks
• Online learning and adaptation under real-time and uncertain wireless environments
• Data-efficient learning and representation for sparse, high-dimensional wireless signals
• AI for network planning, self-optimization, and fault prediction in wireless networks
• Cross-layer ML-driven optimizations for joint sensing, control, and communication
• Co-design of hardware and ML algorithms for low-power and real-time wireless AI
• Trustworthy and explainable AI in high-stakes communication systems

We welcome contributions that push the boundaries at this unique intersection and aim to create an engaging forum for students, scholars, and practitioners worldwide to share insights, discuss progress, and chart future directions in this exciting field. We invite technical papers with up to 6 pages each, vision/position papers with up to 4 pages each, and demo papers with up to 2 pages each (excluding references and appendices), reviewed by our workshop program committee. All submissions must use the official NeurIPS 2025 LaTeX style file for both submission and camera-ready versions. All double-anonymous submissions must use the NeurIPS author kit available here. The review process will be facilitated via OpenReview. Please make sure every author has an OpenReview account ahead of submission. The submission portal can be found here.

Accepted papers will be accessible via this website ahead of the workshop. Our workshop is non-archival and there are no formal proceedings. We allow submissions of manuscripts that have not been accepted by an archival conference, i.e., if your paper is in submission with an archival conference/journal at the time of the workshop submission deadline you are welcome to submit to AI4NextG.

[Please be aware] OpenReview's moderation policy for newly created profiles in the Call for Papers: New profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.

Camera Ready Instruction

Authors of accepted papers should submit their final de-anonymized PDF via OpenReview using the workshop-specific LaTeX template adapted from the NeurIPS style. Final versions may be up to 7 pages for technical papers, 5 pages for vision/position papers, and 3 pages for demo papers (excluding references and appendices). Links to code/data and ethics or impact notes are encouraged. The workshop is non-archival, and subsequent submission elsewhere is permitted.