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Prioritizing Repeated LLM Evaluation for Hidden Failure Discovery
Large language models are commonly evaluated by generating a small number of stochastic responses for each prompt in a benchmark. Because inference budgets are limited, this shallow evaluation may fail to observe low-probability but operationally important failures. A prompt that produces no failure
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