This paper proposes a comprehensive vision for designing large multi-modal models (LMMs) as universal foundation models for AI-native wireless systems. Current efforts on LLMs for wireless networks are limited to single-modal applications, restricting their utility. The proposed framework emphasizes three key capabilities: 1) processing multi-modal sensing data, 2) grounding physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from wireless environment feedback through logical and mathematical reasoning enabled by neuro-symbolic AI. These properties allow LMMs to build universal capabilities for cross-layer networking tasks and intent alignment across domains. Preliminary results show that grounding using RAG improves LMM responses, and they demonstrate enhanced logical and mathematical reasoning compared to vanilla LLMs. The paper also presents open questions and challenges for LMMs, along with recommendations for LMM-empowered AI-native systems. The proposed framework for LMM-empowered AI-native wireless systems includes three key components: 1) multi-modal data fusion, which involves fusing multi-modal sensing information into a shared semantic space; 2) grounding, which involves creating a wireless-specific language through RAG and causal reasoning; and 3) instructibility, which enables dynamic adaptation of signaling and resource allocation strategies based on environmental feedback through logical and mathematical reasoning. The resulting LMM-generated network actions align with 3GPP standards and regulatory norms. The paper discusses challenges in constructing universal foundation models, including network planning, acquiring diverse datasets, and adapting to evolving standards. It also proposes approaches to reduce computational complexity in training and inference for LMMs, contributing to sustainable wireless network goals. The framework and initial experimental results serve as a guide for understanding the essential components necessary for building universal wireless foundation models that can effectively execute diverse cross-layer network functionalities.This paper proposes a comprehensive vision for designing large multi-modal models (LMMs) as universal foundation models for AI-native wireless systems. Current efforts on LLMs for wireless networks are limited to single-modal applications, restricting their utility. The proposed framework emphasizes three key capabilities: 1) processing multi-modal sensing data, 2) grounding physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from wireless environment feedback through logical and mathematical reasoning enabled by neuro-symbolic AI. These properties allow LMMs to build universal capabilities for cross-layer networking tasks and intent alignment across domains. Preliminary results show that grounding using RAG improves LMM responses, and they demonstrate enhanced logical and mathematical reasoning compared to vanilla LLMs. The paper also presents open questions and challenges for LMMs, along with recommendations for LMM-empowered AI-native systems. The proposed framework for LMM-empowered AI-native wireless systems includes three key components: 1) multi-modal data fusion, which involves fusing multi-modal sensing information into a shared semantic space; 2) grounding, which involves creating a wireless-specific language through RAG and causal reasoning; and 3) instructibility, which enables dynamic adaptation of signaling and resource allocation strategies based on environmental feedback through logical and mathematical reasoning. The resulting LMM-generated network actions align with 3GPP standards and regulatory norms. The paper discusses challenges in constructing universal foundation models, including network planning, acquiring diverse datasets, and adapting to evolving standards. It also proposes approaches to reduce computational complexity in training and inference for LMMs, contributing to sustainable wireless network goals. The framework and initial experimental results serve as a guide for understanding the essential components necessary for building universal wireless foundation models that can effectively execute diverse cross-layer network functionalities.