Why do TensorFlow models fail silently when feature encoding order changes?

Feature reordering changes input semantics, leading to incorrect predictions without runtime errors.

TensorFlow models assume fixed feature ordering learned during training. If the order changes during inference, the model interprets features incorrectly, producing valid but wrong outputs. This is a silent failure because shapes remain consistent, but semantics are broken.