AI 中文总结
研究针对无线智能中物理层快速精确推理需求，提出轻量级ScoreFM基础模型。它学习可重复使用分数函数，推理时嵌入特定任务算法作去噪器，支持多样下游任务，案例研究验证其灵活性与有效性。
AI 中文摘要
无线智能不仅需要用于网络范围规划和决策的大型基础模型，还需要一个紧凑的“小脑”进行快速精确的物理层推理。与上层使用的计算密集型架构不同，物理层小脑必须在严格的微秒到毫秒延迟约束下运行。本文提出了ScoreFM，一种为此设计的轻量级基于分数的基础模型。ScoreFM学习可重复使用的分数函数，在推理时，这些先验知识作为即插即用的去噪器嵌入到特定任务的消息传递算法中。案例研究证明了ScoreFM的灵活性和有效性。最后讨论了实现实用无线小脑的未来方向和挑战。
英文摘要
Wireless intelligence requires not only large foundation models for network-wide planning and decision-making, but also a compact "cerebellum" for fast and precise physical-layer inference. Unlike the computation-intensive architectures used at upper layers, the physical-layer cerebellum must operate within stringent microsecond-to-millisecond latency constraints. This article presents ScoreFM, a lightweight score-based foundation model designed for this role. ScoreFM learns reusable score functions that characterize the priors of wireless channels, source signals, and structured interference. During inference, these learned priors are embedded into task-specific message-passing algorithms as plug-and-play denoisers, allowing the same compact score networks to support diverse downstream tasks. This design combines the expressive power of score-based generative learning with the efficiency, interpretability, and modularity of model-based inference. Case studies on channel estimation, localization, and blind semantic communication demonstrate the flexibility and effectiveness of ScoreFM. Finally, we discuss future directions and open challenges toward realizing a practical wireless cerebellum.
