We describe REGLAT’s submission to AlexandriaX-2026 Subtask 3, which requires character-offset error spans in dialectal Arabic machine translations and a Linguistically Motivated Multidimensional Quality Metrics (LQM) category for each span. The system separates localization from classification. Four CAMeLBERT-DA and MARBERTv2 BIO taggers project token probabilities to a shared character grid; whitespace-segment probabilities are then thresholded, bridged, and optionally dilated. A second four-member ensemble classifies detected spans using the marked hypothesis, English source, and translation direction, with class weighting and fitted logit biases. REGLAT ranked fourth out of six teams with an official Overall Score of 0.4021 (Overlap F1 0.4272; Error-Class score 0.3770). Its Exact Match F1 of 0.0041 exposes severe boundary imprecision. We document a scorer mismatch in development-time selection and therefore treat earlier development values as proxy-metric results, not official-metric estimates.