What does this equation mean?

The measurement structure explains the divergence directly. Let R be the event that the retriever’s top- k results contain sufficient evidence to answer the query, and let “correct” mean the generated answer is judged faithful and relevant. Total correctness decomposes as $P(\text{correct}) \;=\; \underbrace{P(R)\,P(\text{correct} \mid R)}_{\text{grounded in retrieved evidence}} \;+\; \underbrace{P(\lnot R)\,P(\text{correct} \mid \lnot R)}_{\text{correct without sufficient retrieved evidence}}$. An end-to-end accuracy score measures the left-hand side only. The two terms on the right are exactly what generation metrics and retrieval metrics separately illuminate, and the second term is the one an aggregate score cannot see at all. P($\text{correct}$ $\mid$ $\lnot$ R) is the probability the model answers correctly despite the retriever having failed — because the underlying language model already carried the answer parametrically, because the question was easy enough to guess, or, in the worst case, because the benchmark’s own answer had leaked into the model’s training data. Whenever that term is large, an end-to-end score can look excellent while the retrieval component the system was supposedly built around contributed nothing to the result.