Wireless Intelligence Needs a Cerebellum: Score-Based Foundation Models Toward Real-Time Physical-Layer Inference

Abstract

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.