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CALL FOR WORKSHOP PAPERS
Following the success of the previous editions, the third IEEE/IFIP Workshop on Generative AI for Network Management 2026 (GAIN2026) focuses again on Generative AI (GenAI) technologies in engineering and operating communication networks, but complements its scope in 2026 by addressing Agentic AI concepts and architectures in the GenAI and network, i.e. Agentic AI technologies and methods will take advantage of or will provide support for GenAI in network management.
Generative AI (GenAI) is an exciting technology that has garnered tremendous attention. Large Language Models (LLM), as part of Generative AI, provided answers that amazed domain experts. GenAI, however, has been shown to be more than a hype. It builds on the profound theory and concepts of generative statistical models that can identify and exploit complex statistical relationships in large amounts of data, such as joint probability distributions.
Agentic AI may enhance GenAI in network management. It may bring new collaboration techniques between AI agents and specific agents that utilize GenAI. This raises questions regarding knowledge sharing among agents, as well as challenges related to interaction, security, trustworthiness, and privacy.
Network Management is traditionally data-driven, particularly in large-scale and public communication networks. For example, these systems produce large error logs, or require large amount of data and information to configure them. Moreover, the service quality is typically constantly monitored and reported, leading to big data sets. In addition, modern communication networks are highly complex in their structure and nowadays don’t only represent physical relationships but also often virtual ones, like slices in SDN, network function virtualization, disaggregated RAN (OpenRAN), Cloud-native network operation, MEC, etc. Furthermore, network management is typically costly, which is often caused by a low degree of automation. The use of data-based AI and, particularly, the application of GenAI might overcoming these impasses in modern communication networks.
The 2026 edition of GAIN will agsin bring together academic researchers from various disciplines (communication networks, data science, operational research) and practitioners from industry. The workshop welcomes scientific papers as well as industrial use case papers. The considered topics in GenAI for network management are initially structured along the well-accepted FCAPS model for network and network service management. However, the workshop is not limited to these topics, and novel contributions on the use of Generative AI and agentic AI in the operation and management of networks and services are welcome.
Specifics of Agentic AI in Network Management
Specifics of Agentic AI in Network Management
Collaborative Intelligence for Agentic AI and Generative AI
Localization and Transparency of Agentic AI
Security and Privacy of Agentic AI
Interaction techniques with Agentic AI and Generative AI for network management
Fault Management
Fault Management
Predictive Maintenance: Using generative AI for proactive network management
Network Troubleshooting using Generative AI, incl. root cause analysis and resolution
Monitoring using Generative AI: Using generative AI for efficient monitoring of network resources
Configuration Management
Network Configuration Automation with Generative AI
Automated Network Design and Deployment using Generative AI
Generative AI for Traffic Management: Optimizing network traffic engineering through AI
Accounting
Ethical Considerations: Addressing Privacy and Security Concerns in AI-Based Network Management
Ensuring fairness between network users using generative AI for optimal resource allocation
Performance
Performance
Network Optimization with AI: Leveraging generative algorithms for network efficiency
Dynamic Resource Allocation: Leveraging generative AI for efficient network resource management
Efficient Network Data Analysis using Generative A
Security
AI-Based Security Protocols: Developing next-generation network security strategies
Anomaly Detection and Response: Utilizing generative AI for enhanced network security
Use Cases
Generative AI for management of IoT, Wireless/RAN, or Core, and Cloud-to-Edge networking
General
General
Prompt Engineering for Network Management Using LLMs
Robustness and Reliability of Generative AI for net. management (incl. benchmarks and datasets)
Scalability Orchestration, Testing and Validation of Generative AI for Network Management
Submission and Important Dates:
Submission and Important Dates:
GAIN 2026 workshop papers:
https://jems3.sbc.org.br/noms2026_gain_workshop
Paper Submission Deadline: Jan. 19, 2026
Feb 2, 2026
Feb. 9, 2026
Notification of Acceptance: Mar 2, 2026
Mar. 9, 2026
Final Camera Ready: Mar. 16, 2026
Mar 22, 2026 (very hard deadline)
Workshop organisers:
Workshop organisers:
Alberto Leon-Garcia, Univ. of Toronto, CA
(alberto.leongarcia@utoronto.ca)
Pal Varga, Budapest Univ. of Technology and Economics, HU
(pvarga@tmit.bme.hu)
Kurt Tutschku, Blekinge Inst. of Technology, SE
(ktt@bth.se)
Author Instructions:
Author Instructions:
Authors are invited to submit original contributions to the workshop that are written in English and that have not been published or submitted for publication elsewhere. Workshop papers must be submitted as PDFs using the IEEE conference double-column format style. A NOMS workshop paper must not exceed six pages for all submissions.
(Style templates can be found here: https://www.ieee.org/conferences/publishing/templates.html)
All submitted papers will be peer-reviewed. For accepted papers, at least one author is expected to register and present the paper in person at the workshop. Accepted and presented papers will be published in the conference proceedings and submitted to IEEE Xplore.
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