AI models can exhibit unpredictable behavior, leading to inconsistent results even with identical prompts. This inconsistency is often due to factors like warm caches, short inputs, or sheer luck, rather than the model being broken. To address this, developers should implement rigorous testing protocols that log multiple runs, record machine-checked output validity, and measure execution time, rather than relying on simple screenshots. Furthermore, tests should explicitly declare all sources of nondeterminism, such as time-dependent functions or external inputs, to prevent AI
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