CAFD improves DNN fault detection by 18.3% on average using a novel concept-based feature from VLMs alongside other signals, outperforming five baselines on multiple models and datasets.
Calibrated neighborhood aware confidence measure for deep metric learning,
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VLAMotor exposes VLA failures via distance-aware uncertainty testing and synthesizes agent-planned repair data to fine-tune models, reporting 49.25% success rate gains in simulation and 57.5% on hardware.
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VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis
VLAMotor exposes VLA failures via distance-aware uncertainty testing and synthesizes agent-planned repair data to fine-tune models, reporting 49.25% success rate gains in simulation and 57.5% on hardware.