arXiv: cs.AI preprintsInternational6 November 2026
Failure Prediction and Classification in Vision-Language-Action Models Via Calibrated Latent Probing
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arXiv:2611.08425v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) enable general-purpose embodied intelligence, but their performance is brittle under distribution shift, where unanticipated failures can damage equipment or endanger people. Deploying VLAs in real-world systems therefore requires a runtime monitor that is timely, calibrated, and informative while being efficient enough to run on the edge. Existing methods fail to fulfill these requirements. We present VLA-Tug, a lightweight probe over the latent layers of a VLA backbone that improves early failure detection
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