What does this equation mean?

The mechanism is not exotic. A benchmark released as a public dataset, a paper, or a web page is exactly the kind of content large-scale pretraining crawls are built to ingest. A survey of benchmark data contamination in large language models lays out the resulting problem directly: leading models trained on web-scale corpora can inadvertently incorporate benchmark data into their training sets, which inflates measured performance in ways that do not reflect genuine capability, and the survey catalogues detection methods and alternative assessment strategies developed specifically to counter it [ 12 ] . For RAG evaluation the consequence is sharper than for a plain language-model benchmark, because contamination corrupts exactly the term this article has been isolating: a contaminated model can produce a correct, fluent answer with the retriever disabled entirely, which means P($\text{correct}$ $\mid$ $\lnot$ R) is inflated by memorisation rather than by any generalisable capability, and every end-to-end score computed against that reference set overstates what the retrieval component is doing.