# Instruction-Execution Progress Estimation

Vision-language navigation agents previously matched a whole instruction against candidate trajectories, so already executed content kept interfering with later decisions. VLN-AIEP couples instruction-execution progress estimation with navigation decision-making: it uses the visited trajectory to suppress embeddings of executed content, then matches candidate locations only against pending content, so progress can be learned directly from navigation decision labels without explicit progress annotations. Across R2R, RxR, CVDN and R2R-CE, the method reaches competitive results on multiple metrics, including 77% success on R2R val-unseen, 3 points above PRET.

Vision-language navigation agents previously matched a whole instruction against candidate trajectories, so already executed content kept interfering with later decisions. VLN-AIEP couples instruction-execution progress estimation with navigation decision-making: it uses the visited trajectory to suppress embeddings of executed content, then matches candidate locations only against pending content, so progress can be learned directly from navigation decision labels without explicit progress annotations. Across R2R, RxR, CVDN and R2R-CE, the method reaches competitive results on multiple metrics, including 77% success on R2R val-unseen, 3 points above PRET.