A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions

• URL: http://arxiv.org/abs/2606.26585v1
• Date: Thu, 25 Jun 2026 04:17:39 GMT
• Title: A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions
• Abstract summary: We propose a multi-level validation and traceable reasoning framework that performs systematic reliability verification of AI-generated decisions.<n>Experiments show that the framework improves executability and reliability of AI scheduling and reduces loss of transient opportunities.<n>These results demonstrate a feasible and verifiable pathway for applying AI to high-reliability astronomical observation scheduling.
• Score: 0.17188280334580194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the gradual introduction of AI into telescope scheduling, AI-based decision-making has shown advantages in handling complex multi-constraint problems. However, its outputs often suffer from inconsistent data references, reasoning errors, and non-executable decisions, limiting applicability in high-reliability observational tasks. In this work, we propose a multi-level validation and traceable reasoning framework that performs systematic reliability verification of AI-generated decisions prior to execution, and enables explicit representation of the reasoning process to support traceable decision-making. The framework integrates data reference validation, logical consistency checks, and observational and instrumental constraint verification to filter and correct invalid decisions. It also introduces atomic reasoning units and their dependency relationships, representing scheduling decisions as a sequence of interconnected reasoning steps that support error localization and post hoc analysis. Experiments show that the framework improves executability and reliability of AI scheduling and reduces loss of transient opportunities. In particular, feedback correction and structured validation of reasoning steps enhance the ability to repair and block erroneous decisions, especially in complex scenarios. Compared with pure AI methods, the framework-enhanced approach maintains flexibility while substantially improving reliability and executability. These results demonstrate a feasible and verifiable pathway for applying AI to high-reliability astronomical observation scheduling.

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