Papers

Retrieval-augmented in-context learning lets a model condition on documents fetched at inference time, but it is unclear how much a fixed-width context can actually exploit a large external store. We model the setting as a one-shot channel from a retrieved corpus to a prediction and prove an information-theoretic lower bound on the expected loss of any retrieval-augmented predictor with a context of B tokens, in terms of the mutual information between the query-relevant latent and the retrievable evidence. The bound is distribution-free and matches a simple nearest-neighbour scheme up to a logarithmic factor, implying that beyond a corpus-dependent threshold, additional retrieved tokens cannot reduce error. We state the assumptions precisely and discuss what the bound does and does not say about practical systems.