Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports

In longitudinal clinical practice, every chest X-ray is read in the
context of the patient's prior exam, and much of what the
radiologist communicates is the change from one visit to the next. To
the best of our knowledge, we present the first training-free
best-of-N sampling scheme for pre-trained chest X-ray report
generators that is explicitly aware of this longitudinal
prior -> current transition. We call it
transition-aware best-of-N sampling: each report is
split into sentences and embedded into an unordered set in
R^d; each (prior, current) pair is reduced to a fixed-dim
\emph{directional} vector via a set-to-set distance designed to encode
the change between the two sets; and candidates are scored by cosine
distance from their candidate transition vector to a cached bank of
ground-truth training transition vectors, aggregated as min or
kNN. We instantiate the framework with four
directional set distances (mean-shift, novelty residual,
directed-Hausdorff anchor, and cost-weighted optimal transport) and
evaluate on a multi-visit AP/PA cohort, running inference under three
prompts on three vision--language generators. Transition-aware
best-of-N outperforms random selection across the board, with the
largest relative gains on the Impression section.