Baseline-Relative Projection Diagnostics for Multimodal Prediction

Multimodal fusion gains depend on the nonlinear baseline used for comparison. We develop a reusable diagnostic protocol that applies empirical multimodally-additive projection (EMAP) to both a fusion head and its baseline. Their original gap separates into a projected gap and excess projection sensitivity. The protocol combines shared references, parameter-matched controls, sealed model selection and paired uncertainty. We characterise finite-reference bias, ranking variance and paired score covariance under explicit assumptions. ANOVA and Shapley relations specify the functional interaction being probed. Experiments cover MM-IMDb, MOSI, MOSEI and Balanced Binary Abstract Scenes VQA. On MM-IMDb, Mamba-style retains projected advantages of 1.412 to 1.533 AUC percentage points over matched concatenation with word2vec features. All projected-gap intervals include zero with sentence-transformer features. On MOSEI, validation selection reduces a source-rate TFN advantage over matched concatenation from 0.753 to −0.001 MAE units. In VQA, MLB-style's original advantage of 2.302 accuracy percentage points becomes a projected gap of −0.005, with interval [−0.432, 0.401]. Four predeclared mechanisms across three new datasets yield distinct patterns of baseline strength and relative projection response. An accompanying library implements projection, paired inference and a common reporting record. The protocol makes these properties directly testable components of a fusion comparison.