This chapter develops the LMs4Net paradigm, where language models function as semantic reasoning engines embedded across wireless optimization, verification, and system control. Unlike the “Network-for-LLMs” perspective of earlier chapters, which focuses on enabling model training and inference, LMs4Net examines how LLMs and SLMs contribute directly to wireless network operation. The chapter shows how models interpret optimization structures, collaborate with classical solvers, refine solutions through feedback, and participate in verification through self-checking, ensembles, and simulation-guided repair. It also analyzes how cloud-, edge-, and device-level models form agentic control loops that integrate with existing management systems, enabling intent interpretation, semantic telemetry, and QoR-driven decision-making. Finally, the chapter illustrates how this architecture supports emerging applications in smart cities, AI-enhanced mobile devices, human-machine collaboration, and cyber-physical systems. Viewed as a whole, LMs4Net characterizes language models not as standalone tools but as coordinated components stems from their ability to collaborate with classical solvers in iterative real-time network intelligence.