模型共享初始化时，隐性特征传播靠权重对齐实现，而非单纯信息传递。

A student model trained on pure uniform noise can still inherit its teacher's digit-classification ability, provided the two share initialization. Previous work proves this transfer is guaranteed when the teacher's learning rate is small enough, but does not explain where in the network the channel lives or what sets its capacity. Working in an MLP distillation setting on MNIST, we show these channels are not purely informational: geometric alignment gates access to the information the channel carries. Shared initialization makes the output projection W_2 a common coordinate key, and KL gradients reshape the student's input projection W_0 until its hidden representations align with the teacher's. We call this covert trait propagation (CTP). Five experiments support this mechanism: channel closure tracks weight drift, not teacher accuracy; freezing W_0 destroys transfer while freezing W_2 leaves it intact; multi-teacher ensembles cancel out despite each teacher carrying comparable label information; and linear centered kernel alignment (CKA) tracks student accuracy at r=0.98 across a continuous initialization sweep. Applying the same geometric lens to cross-token behavioral entanglement (CTBE) in instruction-tuned LLMs, we find the effect appears to be activated by alignment training, acting on an inherited substrate, and that the standard log-ratio metric produces an apparent frequency bias that is largely a circularity artifact.