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It is found that the model's coding proficiency is inversely related to predictive performance in this setting, with the LLM better understood as a generative solver conditioned on context rather than a faithful predictor of solver behavior.
This work argues for a hybrid public-private approach: keeping the benchmark content private to maintain integrity while making the benchmark design public to build trust, which offers a path toward trustworthy measurement of ever-growing data-intensive AI systems.
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