Embedding Temporal Logic (ETL) performs runtime monitoring directly in learned embedding spaces using distance-based predicates composed with temporal operators, supported by conformal calibration for reliable predicate evaluation.
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KeyStone improves task success rates in diffusion-based physical AI models by up to 13.3% by sampling K trajectories in parallel, clustering them in action space, and returning the medoid of the largest cluster.
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Runtime Monitoring of Perception-Based Autonomous Systems via Embedding Temporal Logic
Embedding Temporal Logic (ETL) performs runtime monitoring directly in learned embedding spaces using distance-based predicates composed with temporal operators, supported by conformal calibration for reliable predicate evaluation.
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Geometry Guided Self-Consistency for Physical AI
KeyStone improves task success rates in diffusion-based physical AI models by up to 13.3% by sampling K trajectories in parallel, clustering them in action space, and returning the medoid of the largest cluster.