BanglaVerse is a new benchmark that evaluates multilingual vision‑language models on Bengali culture, covering nine visual domains and expanding to four languages and five Bangla dialects for a total of about 32,200 artifacts. It includes visual question answering and captioning tasks built from 1,152 manually curated images. Experiments show that models perform worse on dialectal variants and that missing cultural knowledge, rather than visual grounding, is the main bottleneck.
By Nurul Labib Sayeedi, Md. Faiyaz Abdullah Sayeedi, Shubhashis Roy Dipta, Mahbub E Sobhani, Rubaya Tabassum, Ariful Ekraj Hridoy, Mehraj Mahmood, Md. Tarek Hasan, Swakkhar Shatabda
MUSE is a new benchmark designed to evaluate large vision‑language models on artistic image understanding within situated educational contexts. It separates image annotation from question generation, offering twelve tasks that cover visual perception, semantic and affective interpretation, cultural understanding, and compositional reasoning across diverse artistic images from Singaporean, Southeast Asian, and Western traditions. The benchmark reveals significant gaps in model performance, especially in affective interpretation and compositional reasoning, and highlights common failure modes for trustworthy educational multimodal systems.
By Luyao Zhu, Xun Wei Yee, Wei Li, Mun Thye Mak, Wee Siong Ng
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:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.
By Bo-An Chang, Yu-Chih Chen
arXiv:2508.13680v5 Announce Type: replace-cross
Abstract: We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (V...
By Vy Tuong Dang, An Vo, Emilio Villa-Cueva, Quang Tau, Duc Dm, Thamar Solorio, Daeyoung Kim
arXiv:2603.14733v2 Announce Type: replace
Abstract: Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos...
By Yue Zhang, Liqiang Jing, Jia Li, Yapeng Tian, Xinya Du, Yunhui Guo, Vibhav Gogate
arXiv:2510. 08543v2 Announce Type: replace-cross Abstract: As Video Large Language Models (VideoLLMs) are deployed globally, it is important to assess their ability to reason across cultural contexts.
By Nikhil Reddy Varimalla, Yunfei Xu, Meng Fan Wang, Arkadiy Saakyan, Smaranda Muresan
arXiv:2608.30498v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduct...
By Qi Li, Zhaojie Kang, Yingjie He, Zheng Lin, Hao Zhang, Guangxin Wu, Yan Gong, Rong Fu, Jianyuan Ni
We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, covering spoken visual question answering and image-gro...
arXiv:2608.30475v1 Announce Type: cross
Abstract: We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, cove...
By Samir Abdaljalil, Hunzalah Hassan Bhatti, Ahlam Bashiti, Farina Amir, Md Arid Hasan, Basel Mousi, Nadir Durrani, Fahim Dalvi, Zien Sheikh Ali, Erchin Serpedin, Hasan Kurban, Mustafa Jarrar, Shammur Absar Chowdhury, Firoj Alam
NormViz introduces a new benchmark, NormViz‑Bench, comprising 3,268 contrastive image pairs from 16 countries that test AI’s ability to recognize culturally relevant visual norms. Each pair differs only in a behavior that changes its cultural interpretation, and images are labeled as conforming, violating, or irrelevant to local norms, requiring both images to be correctly classified. The benchmark shows current VLMs perform poorly, and a complementary training set, NormViz‑Train, offers a path to improve performance by teaching models to link visual perception with cultural significance.
By Akhila Yerukola, Fabrice Y Harel-Canada, Simran Khanuja, Abhinav Sukumar Rao, Ashima Suvarna, Nanyun Peng, Saadia Gabriel, Maarten Sap
arXiv:2508. 05502v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings.
By Yufei Gao, Jiaying Fei, Nuo Chen, Ruirui Chen, Guohang Yan, Yunshi Lan, Botian Shi