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Dproj, the total-variation defect between the language-goal distributions of inference-equivalent full and marginalized graphs, measures this directly and reproduces the full-graph oracle and reduces goal flips against every memory-reducing baseline.
Even with comparable AUROC, standard VPR methods fail to provide valid conformal prediction guarantees under dataset shift, but SAFEVPR achieves empirical validity across diverse conditions by combining patch-based matching with specialized calibration.
