A validation and traceability framework using data checks, logical consistency, constraint verification, and atomic reasoning units to improve reliability of AI telescope scheduling decisions.
The indiscriminate adoption of ai threatens the foundations of academia
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Position paper arguing that multi-agent AI systems can become AI scientists and calling for reformed scientific institutions to support their development with emphasis on verification and dual-use safety.
citing papers explorer
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A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions
A validation and traceability framework using data checks, logical consistency, constraint verification, and atomic reasoning units to improve reliability of AI telescope scheduling decisions.
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AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
Position paper arguing that multi-agent AI systems can become AI scientists and calling for reformed scientific institutions to support their development with emphasis on verification and dual-use safety.