arXiv:2610.30996v1 Announce Type: new Abstract: Power-grid transient stability is assessed by simulating credible faults, but repeating simulations as operating conditions change is costly, motivating faster critical clearing time estimators. Within the AI-ASSIST project, we developed a retrieval-based estimator around three adopted requirements: estimates retain their supporting states, retrieval abstains without compatible support, and support statistics are tested against observed error. Abstention turns estimator uncertainty into a demand on the archive. Exact switching-configuration equality is used. The dense synthetic BUS39 archive covers 99.9996% of 1,044,797 records; the operational ELES archive covers 73.47% of 322,823 records. At query time, 59.37% of ELES states have an earlier same-topology candidate. Under causal availability, retrieval covers 56.34% of records; on records covered under both protocols, mean absolute error increases by 10.0%. No tested support diagnostic ranks first on both grids. Most tested supervised regressors have lower error than retrieval on BUS39, all higher on the matched retrieval-covered ELES population. On ELES, gradient boosting becomes more accurate than retrieval when temporally adjacent states are excluded, so the accuracy trade depends on the archive. Under the adopted compatibility key, archive availability constrains deployment more than conditional accuracy on ELES. Compatible coverage and chronological availability are auditable before an estimator is fitted.