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The intuitive fix for weak recall is to retrieve more: raise k , widen the net, let the generator sort it out. Cuconasu and colleagues ran the first systematic study of exactly this move and found the relationship between added context and accuracy is not monotonic in the direction most engineers assume. Passages that are topically related to the query but do not actually contain the answer measurably hurt accuracy when added to the prompt — worse than passages that are entirely unrelated, some of which left accuracy unchanged or, in their reported experiments, improved it by as much as 35% [ 7 ] . That is a specific, counterintuitive, vendor-independent finding from a controlled study, not a general claim that noise helps; the mechanism they identify is that a near-miss distractor competes for the generator’s attention in a way a genuinely irrelevant passage does not. The practical consequence is that k has an interior optimum that has to be measured against a corpus’s own distribution of near-misses, and that raising k to improve recall can silently move a system further from that optimum rather than closer to it.