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This paper proposes a multi-level validation and traceability framework for AI-generated telescope scheduling decisions, integrating data reference validation, logical consistency checks, and observational constraints to improve executability and reliability in high-reliability astronomical observation tasks.
This paper proposes a method for validating physical admissibility of AI predictions by using a prediction-control interface with kinematic and dynamic conditions. It demonstrates high accuracy in filtering invalid proposals on the LeRobot PushT task.