Large Language Models for Wireless Communications: From Adaptation to Autonomy

--The emergence of large language models (LLMs) has revolutionized artificial intelligence, offering unprecedented capabilities in reasoning, generalization, and zero-shot learning. These strengths open new frontiers in wireless communications, where increasing complexity and dynamics demand intelligent and adaptive solutions. This article explores the role of LLMs in transforming wireless systems across three key directions: adapting pretrained LLMs for core communication tasks, developing wireless-specific foundation models to balance versatility and efficiency, and enabling agentic LLMs with autonomous reasoning and coordination capabilities. We highlight recent advances, practical case studies, and the unique benefits of LLM-based approaches over traditional methods. Finally, we outline open challenges and research opportunities--including multimodal fusion, collaboration with lightweight models, and self-improving capabilities--charting a path toward intelligent, adaptive, and autonomous wireless networks of the future. The rapid advancement of large language models (LLMs) has transformed natural language processing, unlocking capabilities in reasoning, representation learning, and generalization from limited supervision. These models, built on transformer architectures and trained on large-scale text corpora, exhibit remarkable adaptability across tasks and domains. As such, their core strengths--sequence modeling, contextual understanding, and zero-shot inference--are increasingly being explored for applications far beyond language, to include robotics, software engineering, and, more recently, wireless communications. This article investigates how LLMs can be strategically repurposed to address key challenges in modern wireless networks, tracing a trajectory from task-specific model adaptation to the realization of autonomous, agent-driven communication systems. Next-generation wireless systems are characterized by growing complexity and variability.