What does this equation mean?

The formal version is a simple, reportable metric. For a task with a full-input accuracy $\mathrm{Acc}_{\mathrm{full}}$ and an accuracy $\mathrm{Acc}_{\mathrm{ablate}(m)}$ measured with modality m removed, blanked, or replaced with noise, define $\Delta_m = \mathrm{Acc}_{\mathrm{full}} - \mathrm{Acc}_{\mathrm{ablate}(m)}$. A small $\Delta_m$ for a modality the task specification says should matter is the signature of a unimodal shortcut: the model is scoring well on the full-input evaluation without actually depending on that channel. This is not a hypothetical failure mode. Agrawal and colleagues showed that VQA models trained and evaluated under the field’s original data splits could reach strong scores while relying heavily on the language prior in the question rather than the image content, and that rebuilding the dataset so that identical questions paired with different images required different answers collapsed that shortcut and exposed the true, much lower, vision-grounded accuracy [ 3 ] . Geirhos and colleagues generalise the underlying failure beyond any one benchmark: a network trained by gradient descent to minimise a loss will find the least-effort decision rule that satisfies the training objective, and if a shortcut feature is available and correlates with the label on the training and evaluation distributions, the network will use it regardless of whether the shortcut is what the task designer intended to teach — a pattern the authors document across vision, language, and other domains, and one that standard held-out test accuracy does not detect if the shortcut happens to generalise as far as the test set does [ 14 ] .