Dynamic Decision Learning: Test-Time Evolution for Abnormality Grounding in Rare Diseases

Clinical abnormality grounding for rare diseases is often hindered by data scarcity, rendering supervised fine-tuning infeasible and single-pass inference highly unstable. Thus, we propose Dynamic Decision Learning (DDL), a framework that enables frozen LVLMs to refine their decisions across language and visual spaces by optimizing instructions and consolidating predictions under visual perturbations, thereby improving localization quality and producing a consensus-based reliability score that quantifies the model’s confidence. Results on brain-imaging benchmarks, including a rare-disease dataset with 281 pathology types across 3B–72B models, show that DDL improves mAP@75 by up to 105% on rare-disease cases and surpasses adaptation baselines and supervised fine-tuning. Moreover, we show that DDL yields stronger calibration between consensus-based reliability scores and localization accuracy under severe distribution shifts and increasing task difficulty. The code is available at https://github.com/compai-lab/2026-ICML-DDL .