TARING: Training-Anchored Reweighting for Masked Radiology Report Generation

Radiology report generators can assign high probability to corpus-common content even when it is only weakly supported by the current examination. This ambiguity is particularly important for iterative masked generators, where token predictions depend jointly on image evidence and the evolving partial report. We introduce Training-Anchored Reweighting for Iterative Non-autoregressive Generation (TARING), a training-free decoding procedure that contrasts current-study prediction with a deterministic training-mean evidence reference under a matched generative state. At each unresolved position, the same frozen model evaluates the current study and the reference while holding the partial report, refinement time, report support, queried coordinates, and valid actions fixed. TARING reweights the current-study distribution according to the predictive contrast between the two contexts. A KL-regularized objective yields this update in closed form, providing a principled trade-off between reference-relative contrast and fidelity to the original current-study prediction. On MIMIC-CXR-JPG, TARING increases three-seed CheXbert micro-F1 from 0.512 ± 0.006 without reference guidance to 0.534 ± 0.002, primarily through an increase in recall from 0.489 ± 0.006 to 0.525 ± 0.002. Alternative empirical references yield closely matched F1 values, suggesting that the observed effect is not specific to one exact reference construction. Several higher-order lexical-overlap metrics decrease modestly, indicating a trade-off between extracted clinical-label agreement and reference-text similarity. These results characterize the behavior of training-anchored reweighting for one frozen generator and dataset and do not establish clinical factuality or cross-institution generalization.