High-density biosignal recordings are critical for neural decoding and clinical monitoring, yet real-world deployments often rely on low-density (LD) montages due to hardware and operational constraints. This motivates spatial super-resolution from LD observations, but heterogeneous dependencies under sparse and noisy measurements often lead to artifact propagation and false non-local correlations. To address this, we propose CAFE, a plug-and-play rollout generation scheme that reconstructs the full montage in geometry-aligned stages. Starting from the LD channels, CAFE first recovers nearby channels and then progressively expands to more distal regions, exploiting reliable local structure before introducing non-local interactions. During training, step-wise supervision is applied over channel groups and teacher forcing with epoch-level scheduled sampling along the group dimension is utilized to reduce exposure bias, enabling parallel computation across steps. At test time, CAFE performs an autoregressive rollout across groups, while remaining plug-and-play by reusing any temporal backbone as the shared predictor. Evaluated on $4$ modalities and $6$ datasets, CAFE demonstrates plug-and-play generality across $3$ backbones (MLP, Conv, Transformer) and achieves consistently better reconstruction than $5$ representative baselines.

翻译：高密度生物信号记录对于神经解码和临床监测至关重要，然而由于硬件和操作限制，实际部署通常依赖于低密度导联布局。这促使了从低密度观测中进行空间超分辨率重建的需求，但在稀疏且含噪声的测量下，异质性的依赖关系常常导致伪影传播和虚假的非局部相关性。为解决此问题，我们提出了CAFE，一种即插即用的展开生成方案，该方案在几何对齐的阶段中重建完整导联布局。CAFE从低密度通道出发，首先恢复邻近通道，随后逐步扩展至更远端区域，在引入非局部交互之前充分利用可靠的局部结构。在训练过程中，对通道组施加逐步监督，并沿组维度采用教师强制与周期级调度采样以减少曝光偏差，从而实现跨步骤的并行计算。在测试时，CAFE跨组执行自回归展开，同时通过复用任意时序主干网络作为共享预测器保持即插即用特性。在$4$种模态和$6$个数据集上的评估表明，CAFE在$3$种主干网络（MLP、Conv、Transformer）上均展现出即插即用的通用性，并始终优于$5$种代表性基线方法的重建性能。