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ASSERT is a specification-driven measurement pipeline for auditing generative AI systems, which ties reported rates to explicit measurement choices and demonstrates how these choices can affect compliance rates and system rankings.
This paper presents a six-phase blueprint for self-evolving codebases using LLMs, emphasizing convergence toward a specification rather than metrics.