Combining LLMs for Arabic punctuation restoration should help when their errors are complementary, but aggregation failure can reflect either missing coverage, where no predictor proposes the correct label, or failed selection, where the correct label is present but not selected. We investigate this distinction with vote-based, text-blind aggregators that use predictor labels, agreement, position, and neighbouring predictions, but not Arabic words. We evaluate three frontier LLMs and a fine-tuned AraBERT encoder on AraPunc-sub and ABC. The LLMs show strongly correlated errors: when all three are wrong, they choose the same incorrect label in 79.4% of cases on AraPunc-sub and 77.8% on ABC. Yet the four-predictor oracle achieves macro-F₁ scores of 0.857 and 0.778, surpassing the best standalone predictors (0.639 and 0.496). Leave-one-predictor-out analysis shows that AraBERT supplies the largest unique oracle coverage. Standard aggregation recovers only part of this headroom, and no tested aggregator outperforms standalone AraBERT on AraPunc-sub. A selectability probe confirms that some covered disagreements are learnable, but a deployable router offers only modest, corpus-dependent gains. Even a text-aware peer-deliberation step deepens rather than breaks the correlation, as weaker models conform to their peers. Hence, in the tested text-blind regime, the bottleneck is primarily selection rather than coverage, without ruling out text-aware selectors.