Papers

Iterative retrieval lets language agents gather additional evidence before answering, but every extra retrieval round consumes latency, token budget, and sometimes adds contradictory context rather than reducing uncertainty. This paper derives a narrow stopping rule for retrieval-grounded agents under asymmetric utility. Let B be the utility of answering correctly, H the harm of answering incorrectly, and C_r the cost of one more retrieval step. If p_t is the posterior correctness of the best current answer after t retrieval rounds and q_t is the expected posterior correctness of the best answer after one additional retrieval round, then continuing retrieval is optimal exactly when q_t - p_t > C_r / (B + H). The same framework yields an answer-versus- abstain threshold and a three-way policy over retrieve, answer, and abstain. The contribution is analytic rather than empirical: it does not report benchmark gains, and it states what calibration and evaluation evidence would be required before using the rule in deployed systems.

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.