AI-native 6G visions increasingly invoke wireless foundation models, large multimodal models, and wireless world models as the natural endpoint of AI-native networking, drawing an analogy to recent developments in large language models (LLMs). We argue that this analogy is structurally incomplete. The success of LLMs is based on a broad, reusable, and largely self-contained tokenized data substrate, whereas the wireless domain lacks an equivalent data foundation. Unlike text, code, or images, wireless data such as CSI tensors, IQ samples, or scheduler logs are not self-contained: their meaning is configuration-dependent, simulator-conditioned, task-disaggregated, and weakly grounded in operational feedback, all structural bottlenecks that undermine current pre- and post-training recipes. We therefore argue that monolithic models, including mixture-of-experts (MoE) and wireless world models, are not the most realistic near-term path toward deployable AI-native networks. Instead, emerging evidence points toward composable and agentic network architectures, where general reasoning models orchestrate specialized signal processing models, classical algorithms, digital twins, standards-aware retrieval, and safety checks through explicit programmable interfaces.

翻译：AI原生的6G愿景越来越频繁地引入无线基础模型、大型多模态模型和无线世界模型，将其视为AI原生网络的自然终点，这与大型语言模型（LLMs）的最新发展相类比。我们认为，这种类比在结构上是不完整的。LLMs的成功基于广泛、可复用且基本自包含的标记化数据基座，而无线领域缺乏等效的数据基础。与文本、代码或图像不同，无线数据（如CSI张量、IQ样本或调度程序日志）并非自包含：其含义依赖于配置、受模拟器条件制约、按任务分解，并且在运行反馈中缺乏坚实基础——这些都是损害当前预训练和微调方案的结构性瓶颈。因此，我们认为，包括专家混合模型（MoE）和无线世界模型在内的单一模型，并非通向可部署AI原生网络的最现实近期路径。相反，新兴证据指向可组合与代理式网络架构，其中通用推理模型通过显式可编程接口，协调专用信号处理模型、经典算法、数字孪生、标准感知检索及安全校验。