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

• URL: http://arxiv.org/abs/2604.24972v1
• Date: Mon, 27 Apr 2026 20:14:38 GMT
• Title: Dynamic Decision Learning: Test-Time Evolution for Abnormality Grounding in Rare Diseases
• Abstract summary: We propose Dynamic Decision Learning (DDL), a framework that enables frozen large vision-language models (LVLMs) to refine their decisions across both language and visual spaces.<n>We show that DDL improves mAP@75 by up to 105% on rare-disease cases and outperforms adaptation baselines and supervised fine-tuning.
• Score: 23.238920175100258
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinical abnormality grounding for rare diseases is often hindered by data scarcity, making supervised fine-tuning impractical and single-pass inference highly unstable. We propose Dynamic Decision Learning (DDL), a framework that enables frozen large vision-language models (LVLMs) to refine their decisions across both language and visual spaces by optimizing instructions and consolidating predictions under visual perturbations. This process improves localization quality and produces a consensus-based reliability score that quantifies model confidence. Results on brain imaging benchmarks, including a rare-disease dataset with 281 pathology types across models ranging from 3B to 72B parameters, show that DDL improves mAP@75 by up to 105% on rare-disease cases and outperforms adaptation baselines and supervised fine-tuning. Furthermore, DDL demonstrates stronger calibration between reliability scores and localization accuracy under severe distribution shifts and increasing task difficulty. Code is available at: https://lijunrio.github.io/DDL/

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