Integrating large language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling them with numerical computation. However, LLMs lack the precision required for physical-layer optimization, and the scarcity of wireless training data makes domain-specific fine-tuning impractical. We propose BeamAgent, an LLM-aided MIMO beamforming framework that explicitly decouples semantic intent parsing from numerical optimization. The LLM serves solely as a semantic translator that converts natural language descriptions into structured spatial constraints. A dedicated gradient-based optimizer then jointly solves the discrete base station site selection and continuous precoding design through an alternating optimization algorithm. A scene-aware prompt enables grounded spatial reasoning without fine-tuning, and a multi-round interaction mechanism with dual-layer intent classification ensures robust constraint verification. A penalty-based loss function enforces dark-zone power constraints while releasing optimization degrees of freedom for bright-zone gain maximization. Experiments on a ray-tracing-based urban MIMO scenario show that BeamAgent achieves a bright-zone power of 84.0\,dB, outperforming exhaustive zero-forcing by 7.1 dB under the same dark-zone constraint. The end-to-end system reaches within 3.3 dB of the expert upper bound, with the full optimization completing in under 2 s on a laptop.

翻译：[translated abstract in Chinese] 将大语言模型（LLM）集成到无线通信优化中是一个充满前景但具有挑战性的方向。现有方法要么将LLM作为黑盒求解器，要么作为代码生成器，使其与数值计算紧密耦合。然而，LLM缺乏物理层优化所需的精度，且无线训练数据的稀缺使得领域特定微调难以实现。我们提出BeamAgent——一种基于大语言模型的MIMO波束赋形框架，该框架明确将语义意图解析与数值优化解耦。LLM仅充当语义翻译器，将自然语言描述转换为结构化空间约束。随后，专用梯度优化器通过交替优化算法联合求解离散基站站点选择与连续预编码设计问题。场景感知提示机制无需微调即可实现具象空间推理，而基于双层意图分类的多轮交互机制则确保了稳健的约束验证。基于惩罚函数的损失项在释放优化自由度以实现亮区增益最大化的同时，强制施加暗区功率约束。在基于光线追踪的城市MIMO场景实验中，BeamAgent的亮区功率达到84.0 dB，在相同暗区约束条件下比穷举迫零方案高出7.1 dB。端到端系统性能与专家上界的差距仅为3.3 dB，完整优化流程在笔记本电脑上耗时不足2秒即可完成。