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

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/

Workflow Status

Review status

pending

Role

unreviewed

Read priority

later

Vote

Not set.

Saved

no

Collections

Not filed yet.

Next action

Not filled yet.

Reading Brief

No structured notes yet. Add `summary_sections`, `why_relevant`, `claim_impact`, or `next_action` in `papers.jsonl` to enrich this view.

Why It Surfaced

No ranking explanation is available yet.

Tags

No tags.

Similar Papers

BibTeX

@article{li2026dynamic,
  title = {Dynamic Decision Learning: Test-Time Evolution for Abnormality Grounding in Rare Diseases},
  author = {Jun Li and Mingxuan Liu and Jiazhen Pan and Che Liu and Wenjia Bai and Cosmin I. Bercea and Julia A. Schnabel},
  year = {2026},
  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},
  url = {https://arxiv.org/abs/2604.24972},
  keywords = {cs.CL},
  eprint = {2604.24972},
  archiveprefix = {arXiv},
}

Metadata

{}