Learning When to Update: Dynamic Masking for Adaptive Representation Refinement

Deep neural networks typically incorporate the output of each residual transformation with a fixed contribution, even though the relevance and computational role of individual content representations can vary substantially across inputs, features, and network depth. We introduce Dynamic Masking (DyM), a lightweight mechanism that learns an input-dependent coefficient for each content-bearing representation and uses it to regulate how strongly a newly computed update enters the residual stream. DyM therefore serves simultaneously as a computational control mechanism and an inspectable signal of representation regulation, while avoiding the assumption that its coefficients are direct measures of feature importance. We evaluate DyM across language modeling, structural biology, computer vision, and 3D medical image segmentation. In language models, DyM modulation is structured by linguistic category and context, and neutralizing the most strongly modulated tokens increases target negative log-likelihood substantially more than neutralizing position-matched random or weakly modulated tokens. Notably, strongly attenuated tokens are particularly consequential, demonstrating that lower DyM coefficients do not imply lower relevance. In antibody–antigen interface prediction, biologically relevant interface residues exhibit systematically stronger attenuation of representation updates than non-interface residues. DyM also maintains competitive predictive performance, including modest improvements in ImageNet classification and average brain-tumor segmentation. Across domains, the results show that task-relevant representations may be amplified, attenuated, or dynamically reallocated depending on context. These findings position DyM as a general mechanism for exposing and controlling adaptive representation refinement, and not just a conventional feature-importance estimator.