How to read an AI benchmark claim without being fooled
Last revised 2026-08-08 · Initial draft
A lab posts a number. The number is higher than the last number. Coverage follows. Here is how to
work out in about ninety seconds whether the result deserves your attention.
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Was the test set contaminated?
Is the comparison like for like?
How many runs, and what was the variance?
Who chose the benchmark?
Does the benchmark measure what its name says?
What would this look like if it were not real?
Six questions
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