arXiv Machine Learning July 1, 2026 By Andrew Seohwan Yu, Mohsen Hariri, Kunio Nakamura, Mingrui Yang, Xiaojuan Li, Vipin Chaudhary

Medical Image Spatial Grounding with Semantic Sampling

Read the original on arXiv Machine Learning →

arXiv:2603. 14579v3 Announce Type: replace-cross Abstract: Vision language models (VLMs) have shown significant promise in visual grounding for images as well as videos.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 19

Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology

arXiv:2606. 20477v1 Announce Type: cross Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations.

By Yusuf Salcan (Computer Vision Group, University of Freiburg, Germany, CRIION-AI Lab, Freiburg, Germany), Simon Ging (Computer Vision Group, University of Freiburg, Germany, Adaptive & Agentic AI), Robin Schirrmeister (Department of Radiology, Medical Center -- University of Freiburg, Germany), Philipp Arnold (Department of Radiology, Medical Center -- University of Freiburg, Germany), Elmar Kotter (Department of Radiology, Medical Center -- University of Freiburg, Germany), Behzad Bozorgtabar (Adaptive & Agentic AI), Thomas Brox (Computer Vision Group, University of Freiburg, Germany)

llms computer-vision nlp fine-tuning multimodal benchmarks

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Aug 11

MedPixel: A Unified Pixel-Language Model for Medical Reasoning and Segmentation

arXiv:2608. 09818v1 Announce Type: cross Abstract: Reliable medical image understanding requires models to connect clinical language and visual reasoning with pixel-level grounding.

By Haoyu Yang, Meixing Shi, Zengjie Chen, Haoran Sun, Haitao Leng, Xiaoming Shi, Yuxiang Cai, Yankai Jiang

llms computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 16

Thinking with Visual Grounding

arXiv:2606. 16122v1 Announce Type: new Abstract: Visual thinking should not only sound right; it should show its evidence.

By Junkai Zhang, Yihe Deng, Kai-Wei Chang, Wei Wang

llms agents reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Aug 4

Slot2Text: Object-Centric Visual Tokenization for Efficient and Spatially Traceable Surgical MLLMs

arXiv:2608. 01473v1 Announce Type: cross Abstract: Multimodal large language models (MLLM) for surgical scene understanding typically inject hundreds of dense visual tokens into a language model, leading to costly inference and limited spatial traceability for generated answers.

By Guiqiu Liao, Matjaz Jogan, Daniel A. Hashimoto

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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

arXiv AI

Jul 8

Segmentation before Answering: Pixel Grounding for MLLM Visual Reasoning

arXiv:2607. 05798v1 Announce Type: cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have evolved from static perception to interleaved visual-language reasoning, often referred to as ``thinking with images''.

By Yake Wei, Yuan Wang, Fengyun Rao, Jing Lyu, Di Hu

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Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology

MedPixel: A Unified Pixel-Language Model for Medical Reasoning and Segmentation

Thinking with Visual Grounding

Slot2Text: Object-Centric Visual Tokenization for Efficient and Spatially Traceable Surgical MLLMs

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

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