Do Graph-Completion Front Ends Help Mask-Aware Forecasting? A Study of Structured Sensor Outages

Does graph completion help when a forecaster already represents missingness? We study RESOLVE, a two-step clamped completion front end, against four controls using shared structured input outages and unchanged future targets. Across Beijing Air, METR-LA, and PEMS-BAY, the primary matrix contains 45 checkpoints and 720 evaluation units. RESOLVE achieves lower mean absolute error in 17 of 24 designated sensor- and mixed-outage comparisons, but only two of six against a mask-aware diffusion-recurrent model. An equal-parameter validation ablation likewise finds small, uneven gains. Native-input estimates meet a 2% point margin against GraphGRU, whereas risk-conditioned intervals satisfy a descriptive coverage–width criterion in only 10 of 45 cells. Separate three-seed weather and feeder-energy studies yield contrasting rankings: weather favors diffusion and feature propagation, while RESOLVE has the lowest mixed-outage energy mean error. The small energy advantage over the diffusion comparator is not stable to paired-seed resampling. Prior test-aggregate exposure and unavailable primary per-origin evidence limit the primary findings to descriptive comparisons; supplementary bootstrap intervals are sensitivity checks. Overall, the evidence supports neither a universal completion advantage nor an isolated causal benefit, and highlights the need for matched architectural controls and evaluation beyond network-wide average error.