What does this equation mean?

The retrieval literature said the opposite. Lewis and colleagues introduced RAG explicitly as a combination of parametric and non-parametric memory, and the architectural point is that the second is a different kind of object, not an extension of the first [ 1 ] . Their formulation treats the retrieved passage as a latent variable to be marginalised over. Writing x for the query, y for the output, and $\mathcal{Z}_k(x)$ for the top k passages returned by a retriever with parameters $\eta$ : $p(y \mid x) \approx \sum_{z \in \mathcal{Z}_k(x)} p_\eta(z \mid x) \prod_{i=1}^{|y|} p_\theta\left(y_i \mid x, z, y_{1:i-1}\right)$. Read the structure rather than the arithmetic. The generator is never conditioned on the corpus. It is conditioned on z — one span, or a handful — and its output distribution is a weighted account of what those spans support. Everything outside $\mathcal{Z}_k(x)$ has probability zero of influencing the answer, and the system has no representation of what it excluded. REALM made the same commitment on the training side, learning a latent retriever end-to-end so that the model attends over documents drawn from a corpus at pretraining, fine-tuning and inference time, and reporting 4–16% absolute gains on open-domain question answering over prior methods [ 2 ] .