Large Language Models (LLMs) have ushered in a new era of sophisticated text generation, advanced comprehension, and dynamic interaction. Their evolution originates from the early stages of machine learning (ML) and natural language processing (NLP), beginning with statistical models and progressing through neural networks to deep learning (DL) breakthroughs—particularly the rise of Transformer architectures. These advances have enabled models with unprecedented capabilities to process and generate extensive volumes of textual content.

Among these milestones, OpenAI’s Generative Pre-trained Transformer (GPT) series has emerged as a benchmark, surpassing previous architectures in both scale and performance. This evolution has empowered models to achieve near-human levels of language understanding and generation.

While LLMs have demonstrated remarkable performance across many domains, their application to telecommunications has so far been limited. This workshop aims to accelerate progress in this area and pave the way toward domain-specific “Telecom GPTs.”

With this objective in focus, this workshop is organized around two tracks:

1. Integration of Large Language Models and AI agents in future networks;
2. AI challenge on Specializing Large Language Models for Telecom Network troubleshooting (https://zindi.africa/competitions/the-ai-telco-troubleshooting-challenge/).

This workshop invites original contributions on the application of LLMs and AI agents to wireless communication technologies, including but not limited to:

· Efficient wireless transmission strategies assisted by LLMs/Agents
· LLMs/Agents for optimization and management of communication networks
· LLMs/Agents for protocol design, control, and architecture development
· Network diagnostics and troubleshooting using LLMs/Agents
· Enhancements to next-generation communication protocols
· Network data analytics for improved service quality
· Digital-twin technology utilizing LLMs/Agents
· Theories and architectures of large models and agents for wireless networks
· Datasets for training and evaluating LLM/Agent capabilities in telecom
· Strategies to improve inference efficiency and reliability of LLMs
· Network- and device-assisted training/tuning methods for multimodal LLMs
· LLMs/Agents for intent-driven, energy-efficient, and green networks
· Communication and cooperation protocols for AI agents
· Multimodal architectures
· LLM and AI-based resource allocation

Key Dates
Workshop Paper Submission Deadline: 9 February 2026
Notification of Acceptance: 23 February 2026
Camera Ready: 28 February 2026
Workshop Date: 30 March 2026

Workshop Co-chairs:
● Antonio De Domenico, Huawei R&D, France (antonio.de.domenico@huawei.com)
● David López-Pérez, Universitat Politècnica de València, Spain (d.lopez@iteam.upv.es)
● Samson LASAULCE, CNRS (samson.lasaulc@univ-lorraine.fr)