Recall Is Not Enough: A Reader-Context Diagnostic for Budget-Constrained Retrieval-Augmented Generation

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arXiv:2607.00725v2 Announce Type: replace Abstract: Retrieval-augmented generation under a fixed context budget forces a selection problem: only a fraction of the retrieved evidence fits in front of the reader. The field's standard metric, recall@k, is scored on the retrieved set, but the reader consumes the packed context - and once packing must discard evidence, the two come apart. We introduce answer-in-context, a diagnostic that measures whether a gold answer survives into the packed context, and argue it is the quantity budgeted RAG should be optimizing. It carries substantial information beyond retrieval, adding Delta R^2 = 0.17-0.27 over recall across three multi-hop datasets; even among questions where all gold was retrieved, whether packing keeps the answer separates exact match by 4.6x. Two independent interventions confirm the mediation: a packing change that raises document coverage without raising answer-in-context leaves accuracy flat, and prompt compression that destroys the answer span lowers both together. A graded variant extends the diagnostic to free-form answers, where no verbatim span exists. We then show the diagnostic is actionable. Casting reader-context construction as budgeted submodular maximization gives a packer that beats both deployed top-k truncation and LLMLingua-2 compression - across three reader families, four scales, and four budgets, at equal-or-lower token cost. Against a hand-tuned query-focused heuristic, which we show approximates the same objective, it reaches parity, winning outright only where evidence density is the binding …

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