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VLA-Tug: Lightweight Latent Probe Improves Early Failure Detection in Vision-Language-Action Models

Researchers propose VLA-Tug, a lightweight probe over latent layers of Vision-Language-Action models that improves early failure detection and bounds missed-detection and false-alarm rates with minimal inference overhead. It increases early detection by up to 30% over the strongest baseline and maintains valid bounds in up to 86% of runs under task shift. VLA-Tug also predicts failure modes, boosting top-3 accuracy by up to 44% over a majority baseline, and includes a dataset of 3,180 annotated manipulation episodes.