This position paper argues that to achieve Level 5 autonomous 6G networks, the next generation of Artificial Intelligence in Radio Access Networks (AI-RAN) should transition away from fragmented, narrow predictive models and instead adopt multimodal Large Language Models (LLMs) as central reasoning agents. Current AI-RAN architectures rely on disjointed Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL) agents that operate in isolated domains. These narrow models suffer from siloed knowledge, severe brittleness to out-of-distribution dynamics, and a fundamental inability to bridge the intent gap the semantic disconnect between high-level, unstructured operator directives and rigid numerical network configurations. We propose elevating LLMs, or domain-adapted Large Telecom Models (LTMs), to act as the cognitive operating system situated within the RAN Intelligent Controller (RIC), the control and orchestration layer of AI-RAN. In this architecture, LLMs do not replace narrow models but orchestrate them as executable subroutines, dynamically translating human intent into concrete policies and utilizing Retrieval-Augmented Generation (RAG) to autonomously diagnose complex, multi-vendor network anomalies. To make this architectural shift a reality, we call upon the machine learning community to prioritize critical foundational research tailored to the strict constraints of telecommunications, specifically focusing on continuous alignment via network-driven feedback (RLNF), extreme sub-8-bit edge quantization, neuro-symbolic verification to curb hallucinations, and securing orchestration frameworks against adversarial prompt injections.

翻译：本文立场声明指出，为实现5级自主6G网络，下一代无线接入网人工智能（AI-RAN）应摒弃碎片化的狭义预测模型，转而采用多模态大语言模型（LLM）作为核心推理代理。当前AI-RAN架构依赖在孤立领域运行的割裂式深度神经网络（DNN）和深度强化学习（DRL）代理。这些狭义模型存在知识孤岛、对分布外动态变化极度脆弱，且无法弥合意图鸿沟——即高层非结构化操作指令与刚性数值网络配置之间的语义断裂。我们提议将LLM或领域适配的大型电信模型（LTM）提升为认知操作系统，部署于AI-RAN的控制与编排层——RAN智能控制器（RIC）中。在此架构中，LLM并非取代狭义模型，而是将其编排为可执行子程序，动态将人类意图转化为具体策略，并利用检索增强生成（RAG）自主诊断复杂的多供应商网络异常。为使这一架构转型成为现实，我们呼吁机器学习社区优先开展适应电信严苛约束的关键基础研究，特别聚焦于通过网络驱动反馈（RLNF）实现持续对齐、极端低于8比特的边缘量化、基于神经符号验证抑制幻觉，以及保护编排框架免受对抗性提示注入攻击。