Vision-language models (VLMs) often struggle with culturally specific content — a challenge largely overlooked by existing benchmarks that focus on dominant languages and globalized datasets. We introduce TᴀɪᴡᴀɴVQA, a VQA benchmark designed for Taiwanese culture to evaluate recognition and reasoning in regional contexts. TᴀɪᴡᴀɴVQA contains 2,736 images and 5,472 manually curated questions covering topics such as traditional foods, public signs, festivals, and landmarks. The official benchmark set includes 1,000 images and 2,000 questions for systematic assessment, with the remainder of the data used as training material. Evaluations on state-of-the-art VLMs reveal strong visual recognition but notable weaknesses in cultural reasoning. To address this, we propose a data augmentation strategy that combines human-annotated and synthesized dialogues to enhance cultural understanding. Fine-tuning yields significant gains on TᴀɪᴡᴀɴVQA while maintaining stable performance on other multimodal tasks. To further explore the models’ cultural understanding, we conducted an open-ended question answering experiment. The results indicate a notable decline in cultural knowledge generation ($\approx$10–20\%), suggesting challenges remain. TᴀɪᴡᴀɴVQA offers a scalable framework for building culturally grounded AI models in low-resource cultures, promoting diversity and fairness in multimodal AI. Our dataset and code are publicly available on [Hugging Face](https://huggingface.co/datasets/hhhuang/TaiwanVQA) and [GitHub](https://github.com/hhhuang/TaiwanVQA).

In Chinese studies, understanding the nuanced traits of historical figures, often not explicitly evident in biographical data, has been a key interest.However, identifying these traits can be challenging due to the need for domain expertise, specialist knowledge, and context-specific insights, making the eprocess time-consuming and difficult to scale.Our focus on studying officials from China's Ming Dynasty is no exception.To tackle this challenge, we propose MingOfficial, a large-scale multi-modal dataset consisting of both structured (career records, annotated personnel types) and text (historical texts) data for 13, 031 officials.We further couple the dataset with a graph neural network (GNN) to combine both modalities in order to allow investigation of social structures and provide features to boost down-stream tasks.Experiments show that our proposed MingOfficial could enable exploratory analysis of official identities, and also significantly boost performance in tasks such as identifying nuance identities (e.g.civil officials holding military power) from 24.6% to 98.2% F 1 score in holdout test set.By making MingOfficial publicly available at https://data.depositar.io/ en/dataset/ming_official as both a dataset and an interactive tool, we aim to stimulate further research into the role of social context and representation learning in identifying individual characteristics, and hope to provide inspiration for computational approaches in other fields beyond Chinese studies.