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Kaggle benchmark reveals identical AI scores can mask different types of errors

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A developer submitted a fictional data-normalization benchmark to Kaggle, testing how AI models interpret spreadsheet data. Two models scored 26 out of 28, but their failures differed: one made a decision error by declining to choose, while the other made a representation error by omitting required decimal places. The benchmark's 28 cases covered monetary values, missing values, dates, and identifiers, with strict rules requiring specific JSON output formats. The test, which evaluated Gemini, Claude, and Gemma models, demonstrated that a leaderboard score alone cannot distinguish between fundamentally different kinds of model errors.

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Kaggle benchmark reveals identical AI scores can mask different types of errors · ShortSingh