Rows Are Not Evidence Units: Evidence-Unit Invariance in Weak Temporal Localization

Weak temporal annotations often reach the learner as table rows, but row count can change without changing the underlying supervision. A one-to-many join, selective row replication, or metadata expansion may therefore change fitted preprocessing, the relative weight of weak statements, checkpoint selection, and predictions. We define evidence-unit invariance: alternative row materializations of the same evidence should define the same learning problem. We enforce this property with quotient learning, which compiles stored rows into canonical evidence classes before preprocessing, training, and validation. Across seven temporal datasets, representing every eligible range statement by five rows changes ordinary segment F1 on all seven, with absolute changes from 0.004 to 0.228. Quotient learning leaves the clean runs unchanged and gives zero response to the same materializations. The effect persists when preprocessing is fitted once per acquisition, and separate interventions show contributions from training-risk weighting and validation-based model selection. We also evaluate six common table transformations across three dataset-model configurations. Ordinary segment F1 changes in 12 of 18 combinations and held-out probabilities in 13. Row multiplicity can therefore redefine the effective learning problem even when the underlying evidence is unchanged.