This paper presents a comprehensive vision for designing *universal foundation models* (LMMs) tailored to the unique needs of next-generation wireless systems, aiming to facilitate the deployment of *AI-native networks*. Unlike traditional language models, LMMs are designed to process multi-modal sensing data, ground physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and enable dynamic network adaptation through logical and mathematical reasoning facilitated by neuro-symbolic AI. The proposed framework addresses the limitations of current LLMs in wireless applications, such as lack of grounding, hallucinations, and instructibility. Key contributions include: 1. **Multi-modal Data Fusion**: Integrating multi-modal sensing information into a shared semantic space to enable efficient training of universal foundation models. 2. **Grounding**: Using RAG and causal reasoning to create a wireless-specific language and understand the physical symbol meanings and their relations. 3. **Instructibility**: Facilitating transparent interactions between the wireless environment and LMMs through online reinforcement learning (RL) and neuro-symbolic AI, enabling dynamic adaptation and logical mathematical reasoning. Experimental results demonstrate the effectiveness of grounding using RAG in LMMs, showing improved accuracy and reduced hallucinations compared to vanilla LLMs. The paper also presents a use case for intent-based management, highlighting the improved logical and mathematical reasoning capabilities of LMMs. Additionally, the paper discusses challenges and open questions, such as planning capabilities, training diverse datasets, and building sustainable LMMs for wireless networks. The conclusion offers recommendations for advancing the field of LMMs in AI-native wireless systems.This paper presents a comprehensive vision for designing *universal foundation models* (LMMs) tailored to the unique needs of next-generation wireless systems, aiming to facilitate the deployment of *AI-native networks*. Unlike traditional language models, LMMs are designed to process multi-modal sensing data, ground physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and enable dynamic network adaptation through logical and mathematical reasoning facilitated by neuro-symbolic AI. The proposed framework addresses the limitations of current LLMs in wireless applications, such as lack of grounding, hallucinations, and instructibility. Key contributions include: 1. **Multi-modal Data Fusion**: Integrating multi-modal sensing information into a shared semantic space to enable efficient training of universal foundation models. 2. **Grounding**: Using RAG and causal reasoning to create a wireless-specific language and understand the physical symbol meanings and their relations. 3. **Instructibility**: Facilitating transparent interactions between the wireless environment and LMMs through online reinforcement learning (RL) and neuro-symbolic AI, enabling dynamic adaptation and logical mathematical reasoning. Experimental results demonstrate the effectiveness of grounding using RAG in LMMs, showing improved accuracy and reduced hallucinations compared to vanilla LLMs. The paper also presents a use case for intent-based management, highlighting the improved logical and mathematical reasoning capabilities of LMMs. Additionally, the paper discusses challenges and open questions, such as planning capabilities, training diverse datasets, and building sustainable LMMs for wireless networks. The conclusion offers recommendations for advancing the field of LMMs in AI-native wireless systems.