Aesthetic Visual Reasoning with Multiple Images

Aesthetic evaluation in photography often depends on collections rather than single images, as a photographer’s style emerges through recurring patterns and themes across multiple works. This motivates moving beyond single-image assessment toward group-level aesthetic modeling, which better captures holistic characteristics and supports applications such as photo recommendation, education, and style transfer. Existing Multimodal Large Language Models (MLLMs), however, largely focus on single-image or pairwise comparisons, which limits their group-level reasoning due to two key limitations: the absence of datasets with group-level aesthetic annotations and insufficient multi-image understanding in current models. To address these gaps, we introduce the first dataset for group-level aesthetic reasoning in photography, analyze the behavior of MLLMs in multi-image contexts, and uncover some unique properties of aesthetic reasoning across image groups. Building on these insights, we fine-tune a multi-image MLLM and introduce a lightweight visual triager that re-orders images based on their perceived difficulty, addressing the model’s sensitivity to image order and improving inference on challenging images. Experiments demonstrate that our approach significantly outperforms existing models.