arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.
By Daniela Baiamonte, Elena Fano, Matteo Gabburo, Stefano Simonazzi, Leonardo Rigutini, Andrea Zugarini
arXiv:2606. 05531v1 Announce Type: cross Abstract: Despite the rapid progress of Vision-Language Models (VLMs), the field lacks benchmarks that rigorously diagnose their true reasoning abilities and chart meaningful progress toward human-like multimodal intelligence.
By Mohammad Mahdi Abootorabi, Omid Ghahroodi, Anas Madkoor, Marzia Nouri, Doratossadat Dastgheib, Mohamed Hefeeda, Ehsaneddin Asgari
arXiv:2606. 13572v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios.
By Tanmoy Kanti Halder, Akash Ghosh, Subhadip Baidya, Arijit Roy, Sriparna Saha
arXiv:2603. 19957v2 Announce Type: replace-cross Abstract: Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one or more anatomical sites; yet existing pathology vision-language models (VLMs) reduce this output to a flat label or free-form text.
By Ruicheng Yuan, Zhenxuan Zhang, Anbang Wang, Liwei Hu, Xiangqian Hua, Yaya Peng, Jiawei Luo, Guang Yang
Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios. This gap is critical in regions like rural India, where patients often express complex medical queries in native Indic languages and rely on multimodal inputs such as medical images.
arXiv:2603. 27223v2 Announce Type: replace-cross Abstract: We present EuraGovExam, a multilingual and multimodal benchmark sourced from real-world civil service examinations across five representative Eurasian regions: South Korea, Japan, Taiwan, India, and the European Union.
By Jaeseong Kim, Chaehwan Lim, Sang Hyun Gil, Suan Lee