arXiv AI June 10, 2026 By Bruce Changlong Xu, Lan Wu, Alexander Ryu

A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks

arXiv:2606. 10066v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely downloadable for years, yet reported accuracy assumes these examples were absent from pretraining.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 2

Cross-modal linkage risk in clinical vision-language models

arXiv:2606. 02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence.

By Soroosh Tayebi Arasteh, Mahshad Lotfinia, Sven Nebelung, Daniel Truhn

llms rag fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Aug 27

What Do Medical Vision-Language Models Learn in Radiology? Transfer, Alignment, and Source-Proxy Leakage Under Distribution Shift

The paper investigates how medical vision‑language models (VLMs) behave when faced with distribution shifts such as changes in acquisition domain, supervision, or evaluation protocol. Using datasets like NIH ChestXray14, CheXpert, PadChest, and OpenI, the authors isolate cross‑dataset visual transfer, evaluate multimodal alignment, and quantify source‑proxy leakage in frozen embeddings. They find that self‑supervised visual initialization improves transfer, adversarial adaptation is only marginally helpful, and that multimodal retrieval performance drops under external stress tests while source‑proxy information remains recoverable, highlighting hidden failure modes in medical VLMs.

By Ayoub Louaye Bouaziz, Lokmane Chebouba, Yassine Himeur

llms rag multimodal safety

arXiv AI

Jun 17

Vision-language models for chest radiography do not always need the image

arXiv:2606. 17710v1 Announce Type: cross Abstract: Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image.

By Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh

llms multimodal benchmarks

arXiv AI

6d ago

From Reward Signal to Visual Utility: A Controlled Audit of Medical VLM Post-Training

arXiv:2609. 31450v1 Announce Type: cross Abstract: Medical vision-language model (VLM) post-training is commonly evaluated through answer accuracy.

By Wang Jingxin

llms fine-tuning multimodal

arXiv AI

Jul 21

Med-OPD: Improving Medical Vision-Language Models via Evidence-Aware On-Policy Distillation

arXiv:2607. 16303v1 Announce Type: cross Abstract: Medical Vision-Language Models (Med-VLMs) require reliable reasoning from fine-grained visual evidence, yet existing models can produce plausible clinical answers by relying on language priors or medical templates rather than truly attending to diagnosis-critical regions.

By Yunhang Qian, Jiaquan Yu, Jiawei Liu, Meng Wang, Hongwei Bran Li, Xiaobin Hu

llms fine-tuning efficiency multimodal safety

arXiv AI

Jul 29

Why Does Grounding Hurt Medical VQA? Benchmarking, Diagnosis, and Fine-Tuning of Vision-Language Models

arXiv:2604. 27720v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly applied to medical visual question answering (Med-VQA), yet whether they can \emph{localize} the evidence behind their answers---a prerequisite for clinical auditability---is poorly characterized.

By Xupeng Chen, Binbin Shi, Chenqian Le, Qifu Yin, Lang Lin, Haowei Ni, Ran Gong, Panfeng Li

llms nlp fine-tuning multimodal benchmarks

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Cross-modal linkage risk in clinical vision-language models

What Do Medical Vision-Language Models Learn in Radiology? Transfer, Alignment, and Source-Proxy Leakage Under Distribution Shift

Vision-language models for chest radiography do not always need the image

From Reward Signal to Visual Utility: A Controlled Audit of Medical VLM Post-Training

Med-OPD: Improving Medical Vision-Language Models via Evidence-Aware On-Policy Distillation

Why Does Grounding Hurt Medical VQA? Benchmarking, Diagnosis, and Fine-Tuning of Vision-Language Models