EQUAL INFORMATION IS NOT ENOUGH: ENCODER RANKINGS DEPEND ON INTERFACE REALIZATION

Encoder comparisons are often interpreted architecturally once the information supplied to each model has been matched. We show that information matching is necessary but not sufficient: the same information can be realized through heterogeneous model interfaces in ways that induce different learning problems and different encoder rankings. We separate declared information φ(X) from its architecture-specific realization ρA, writing performance as Y (A, ρA(φ(X)); T ) under a fixed learning protocol T . In camera–LiDAR correspondence, all controlled inputs encode exactly the same occupied-cell set; an audit over 13,935 validation frames finds zero mismatched cells. Holding architecture, parameter count, optimization, comparator, gallery and evaluation fixed, changing only the realization reverses SparseConv3D’s ranking against a raster encoder on RADIATE (−0.0729 → +0.1206) and Boreas (−0.0115 → +0.0618); all four comparisons are resolved under the preregistered criterion. The effect extends to PointPillars on both datasets: on RADIATE an unresolved +0.0073 becomes a resolved +0.0569, and on Boreas an already-resolved +0.0032 becomes +0.0451, with direct realization effects of +0.0496 and +0.0419. The SparseConv3D–PointPillars ordering reverses on both datasets as well. We therefore distinguish information equivalence from interface realization and introduce a realization-aware Declare–Realize–Audit protocol. Architectural attribution requires controlling not only what information is available, but how that information is exposed to the learner.