Silent Broadcasting Can Ruin Your Model

Tensor broadcasting in frameworks like PyTorch and TensorFlow can introduce silent, hard-to-debug bugs by operating on tensors with mismatched shapes without raising an error. A common mistake, such as combining tensors of shape (N, 1) and (N,), results in an unintended (N, N) matrix that corrupts the intended calculation. This can cause regression models to incorrectly optimize for the target's mean value or destroy credit assignment in reinforcement learning. Recommended solutions include adding explicit shape assertions in code, using shape-safe libraries like einops, and writing unit tests to catch these mismatches.