InteriorAlloc: Worst-Case Interior Allocation and Statement-Level Credit for Radiology Report Generation

Radiology report generation is trained and judged against one similarity summed over a whole document, while a clinician acts on the individual statements that document commits to. That summation cannot place a worst-case deficit inside an evidence source, credit a statement by its type, or keep a corrected report within what the generator can deliver, and a weight on the source leaves the within-source deficit untouched. We propose InteriorAlloc, built on one insight: robustness, credit and repair are three readings of one allocation, so a single update carries all three. The allocation is solved exactly inside each evidence source by convex duality, with the dual derivative at the returned root at most $2.03\times 10^{-13}$; each typed statement is credited by exact leave-one-out under that allocation; and the action set is closed under repair, so every corrected report stays deliverable. Every reading is taken on four generated populations that carry the names of public corpora and are drawn from one operating point fixed by published figures, three at a fixed shift, none a measurement on the corpora themselves. Against three objective forms drawn from four published systems, InteriorAlloc reads best in 12 of 18 main-table columns, CheXbert F1 on every carrier among them, 0.449 against 0.4173 on MIMIC-CXR, and the stand-alone-credit or R2Gen form leads the other six. No repair inside the closure introduces a new error, while the three rewrites of a delivered report read new-error rates of 0.0867 to 0.1515, and on every carrier the closure delivers fewer pooled false positives than the same policy with the closure removed. Against a uniform within-source arm, 0.9372 of the true-positive mass on which the two arms' reports differ cancels in pooled counts, so the reallocation InteriorAlloc performs is visible only at the statement level a summed score cannot read.