Residual connections are a cornerstone of modern deep neural networks, facilitating stable gradient propagation and maintaining representational expressiveness. Conventional residual formulations typically combine identity mappings and functional transformations with equal weight, without considering the input-dependent importance of each component. This uniformity restricts the model’s capability to adaptively regulate information flow. We propose a novel Contextual Modulation Training (CoMT) framework that intro…