arXiv AI June 12, 2026 By Shayan Mohammadizadehsamakosh, Pritam Sarkar, Leonid Sigal, Ali Etemad, Elham Dolatabadi

Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs

arXiv:2606. 12590v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved strong performance across medical imaging tasks, yet they remain prone to factual inconsistencies, poor visual grounding, and misalignment with clinically meaningful feedback.

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 Computation and Language

Aug 21

Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment

arXiv:2608. 19825v1 Announce Type: cross Abstract: Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems.

By Yunseo Lee, Hyun Jun Kim, Heeseung Shin, Changwon Lim

llms rag multimodal safety data-engineering

arXiv Computer Vision

3d ago

PLRS-IC: A Dual-Calibration Framework for Chest X-Ray Vision-Language Alignment

arXiv:2609.39266v1 Announce Type: new Abstract: Fine-grained vision-language alignment in chest radiography enables zero-shot classification, grounding, and segmentation without task-specific annotat...

By Qixing Zhao, Jinpeng Li

computer-vision multimodal benchmarks safety

arXiv AI

Aug 6

EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment

arXiv:2608. 04472v1 Announce Type: cross Abstract: The development of foundation models (FMs) is crucial for advancing endoscopic image analysis.

By Zhenyu Yi, Jianwei Xu, Yue Hu, Zhongwei Qiu, Sijing Li, Liang Huang, Bin Lv, Ling Zhang, Yingda Xia

efficiency multimodal safety

arXiv Machine Learning

Jun 26

Just how sure are you? Improving Verbalized Uncertainty Calibration in Medical VQA

arXiv:2606. 27023v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) applied to Medical Visual Question Answering (VQA) tend to produce overconfident outputs regardless of actual correctness, and existing verbalized confidence calibration methods, developed primarily for text only LLMs, do not account for the multimodal nature of medical image understanding.

By Eren Senoglu, Federico Toschi, Nicolo Brunello, Andrea Sassella, Mark James Carman

llms nlp fine-tuning multimodal benchmarks safety

arXiv AI

Jun 24

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.

By Harshvardhan Saini, Samyak Jha, Yiming Tang, Dianbo Liu

llms rag agents fine-tuning multimodal benchmarks safety

arXiv AI

Jun 6

EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models

arXiv:2606. 06379v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generation.

By Qiwei Zeng, Hao Wang, Jinghao Lin, Shuchang Ye, Yuezhe Yang, Yige Peng, Haoyuan Che, Jinman Kim, Lei Bi

llms rag multimodal safety

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Related stories

Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment

PLRS-IC: A Dual-Calibration Framework for Chest X-Ray Vision-Language Alignment

EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment

Just how sure are you? Improving Verbalized Uncertainty Calibration in Medical VQA

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models