# Gfm4Mtsad

Multivariate time-series anomaly detection has long relied on graph detectors trained from scratch on limited target-domain data, whose learned sensor topologies are prone to spurious correlations and cannot reuse relational priors from pretrained graph models. Gfm4Mtsad wraps a frozen graph foundation model in three peripheral modules: SICA aligns temporal features with normal-connectivity priors and restores projection-lost detail, MAPS synthesizes near-boundary pseudo-anomalies through residual-preserving local dependency breaks, and TGEC re-ranks multi-granular evidence only in the upper score tail. Across MSL P-15, SMAP T-3 and SMD 1-1 with AnomalyGFM and OwlEye backbones, the adaptation reaches best or second-best results on most metrics.

Multivariate time-series anomaly detection has long relied on graph detectors trained from scratch on limited target-domain data, whose learned sensor topologies are prone to spurious correlations and cannot reuse relational priors from pretrained graph models. Gfm4Mtsad wraps a frozen graph foundation model in three peripheral modules: SICA aligns temporal features with normal-connectivity priors and restores projection-lost detail, MAPS synthesizes near-boundary pseudo-anomalies through residual-preserving local dependency breaks, and TGEC re-ranks multi-granular evidence only in the upper score tail. Across MSL P-15, SMAP T-3 and SMD 1-1 with AnomalyGFM and OwlEye backbones, the adaptation reaches best or second-best results on most metrics.