VERDICT: Visual Extraction-Result Decoder with Integrated Confidence and Traceable Evidence

Enterprises run schema-guided structured field extraction on documents with frontier multimodal models, and accuracy keeps improving as new models and systems arrive. Two things have not kept pace: the evidence for each extracted value, meaning the location of the document it was read from, and how far to trust the value and that evidence. Neither is a component of most extraction systems today, so we build them as a framework that attaches to an existing extractor and leaves it untouched. We present VERDICT, a plug-in framework that uses a small Vision--Language Model (VLM) to ground/localize the evidence once per document over a fixed external extractor's output, then calibrates value and evidence confidence. Fine-tuning the VLM for evidence localization substantially improves visual evidence quality and, more importantly, expands VERDICT's selective-prediction operating range. At a strict confidence threshold 0.80, VERDICT with the fine-tuned VLM gives usable precision--coverage trade-offs for both value and evidence confidence across the in-distribution and out of distribution datasets, whereas the frozen setting often collapses to little or no coverage. Running an open-weight VLM for localization rather than a closed API makes its internal states available to the calibrator, and they are worth more than output log-probabilities: in-distribution AUROC rises from 0.730 to 0.914 for value confidence and from 0.681 to 0.906 for evidence confidence. This advantage does not survive a distribution shift. We show that the signal is displaced rather than destroyed, and that labeling roughly 120 fields from the target dataset takes evidence confidence from 0.641 to 0.905 on one dataset and from 0.675 to 0.829 on the other.