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LLMs can synthesize formal safety rules from natural language goals, offering a path to more robust and verifiable AI systems in safety-critical domains.
LLM-generated debugging explanations are often vague or misleading, but this work shows you can make them dramatically better by carefully curating the context provided to the LLM.