  Large language models (LLMs) and foundation models have been recently touted
as a game-changer for 6G systems. However, recent efforts on LLMs for wireless
networks are limited to a direct application of existing language models that
were designed for natural language processing (NLP) applications. To address
this challenge and create wireless-centric foundation models, this paper
presents a comprehensive vision on how to design universal foundation models
that are tailored towards the deployment of artificial intelligence (AI)-native
networks. Diverging from NLP-based foundation models, the proposed framework
promotes the design of large multi-modal models (LMMs) fostered by three key
capabilities: 1) processing of multi-modal sensing data, 2) grounding of
physical symbol representations in real-world wireless systems using causal
reasoning and retrieval-augmented generation (RAG), and 3) enabling
instructibility from the wireless environment feedback to facilitate dynamic
network adaptation thanks to logical and mathematical reasoning facilitated by
neuro-symbolic AI. In essence, these properties enable the proposed LMM
framework to build universal capabilities that cater to various cross-layer
networking tasks and alignment of intents across different domains. Preliminary
results from experimental evaluation demonstrate the efficacy of grounding
using RAG in LMMs, and showcase the alignment of LMMs with wireless system
designs. Furthermore, the enhanced rationale exhibited in the responses to
mathematical questions by LMMs, compared to vanilla LLMs, demonstrates the
logical and mathematical reasoning capabilities inherent in LMMs. Building on
those results, we present a sequel of open questions and challenges for LMMs.
We then conclude with a set of recommendations that ignite the path towards
LMM-empowered AI-native systems.
