Framework Validates AI Telescope Scheduling for Reliability and Traceability.

As artificial intelligence increasingly assists with complex tasks like telescope scheduling, ensuring the reliability and executability of its decisions becomes paramount. AI-generated schedules can suffer from inconsistencies, reasoning errors, or non-executable commands, which are critical issues in high-reliability observational astronomy. This research introduces a comprehensive framework designed to address these challenges. The proposed multi-level validation and traceable reasoning framework systematically checks AI decisions before execution. It incorporates data reference validation, logical consistency checks, and verification against observational and instrumental constraints to filter out and correct invalid decisions. Furthermore, the framework introduces atomic reasoning units and their dependencies, allowing scheduling decisions to be represented as a sequence of interconnected steps. This explicit representation supports error localization and facilitates post-hoc analysis, significantly improving the executability and reliability of AI scheduling while reducing the loss of transient observational opportunities.