Rotate Before You Update: Deeper Test-Time Adaptation for Graph Models
TL;DR for operators A deployed graph model may encounter a new network, molecule population, or sensor regime and still need to predict before any verified label exists. The operational question is whether it should remain fixed or make a tightly bounded adjustment using only information available in the incoming unlabeled sample. T3R offers the second option. Across nine DiTEC-WDN benchmarks, one update reduced average RMSE from 0.5023 under ERM to 0.3095, a 38.38% reduction, although it did not lead the average NSE metric. The method learns during training how an auxiliary objective can provide proxy updates for deeper prediction layers when the true supervised correction is unavailable. ...