While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is no dedicated framework for assessing human-centric alignment in vision-language systems.

We offer two contributions to address this gap. First, we introduce Anthropogenic Regional Adaptation: a regional-adaptation framework that aims to optimize model relevance to specific regional contexts while ensuring the retention of global generalization capabilities. Second, we present a simple, but effective adaptation method named Geographical-generalization-made-easy (GG-EZ), which utilizes regional data filtering and model merging.

Through comprehensive ablations on 3 VL architectures: large vision-language models (LVLM), text-to-image diffusion models, and vision-language embedding models, and a case study in Southeast Asia (SEA) regional adaptation, we demonstrate the importance of Anthropogenic Regional Adaptation and GG-EZ, showing 5-15% gains in cultural relevance metrics across SEA while maintaining over 98% of global performance and even occasionally surpassing it.

Our findings establish Anthropogenic Regional Alignment as a foundational paradigm towards applicability of multimodal VL architectures in diverse regions and demonstrate a simple-yet-effective baseline method that optimizes regional value alignment while preserving global generalization.

Large Language Models (LLMs) show impressive reasoning capabilities, yet their understanding of culturally specific social dynamics remains limited. We present KaiToM, a Theory of Mind (ToM) benchmark for Thai cultural contexts, focusing on Kreng-Jai, a uniquely Thai concept of social reluctance and consideration. Extending TactfulToM, we construct 882 conversations (39.2K questions) spanning three Kreng-Jai dimensions: White Lies, Avoidance of Confrontation, and Fear-Based Respect. To disentangle language proficiency from cultural knowledge, the dataset includes Thai scenarios at varying cultural depths, general English scenarios, and English translations of Thai content. We evaluate Thai-specialized (Typhoonv2.1, SEA-LION-v3), open-weight (Gemma-3, DeepSeek-V3.2, Kimi-K2, Qwen), and proprietary models (GPT-4o-mini, GPT-5.1, Gemini-3-Flash). Models achieve only moderate performance, struggle with Fear-Based Respect, and cannot reliably identify specific Kreng-Jai types, a failure shared by Thai-specialized models. Human evaluation shows that native speakers significantly outperform models on culturally loaded tasks. Performance also degrades in deep relative to surface-level cultural contexts, and generalist models often perform better on English translations than on the original Thai, suggesting that models capture surface cues but lack deeper cultural alignment. KaiToM supports the development of culturally aware systems for low-resource languages. Code is available at https://github.com/Augustus2011/KAI-ToM.