Your model already knows the answer: how benchmark answers leak into LLMs
This article explores the 'contamination problem' in AI benchmarks, where models inadvertently memorize answers to test questions during training. It argues that public data leaks make it difficult to distinguish between genuine reasoning and simple recall.
The best test of an AI model is a real world problem with a known answer. It is also the easiest test to cheat. An AI model has read much of the internet, so it may have met your question, the answer or both long before you asked. A high score then hides two very different things: a model that reasoned its way to the answer, and one that is repeating something it already read. Telling those apart is the contamination problem , and it sits under a surprising share of the benchmarks the field relies on.
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