We introduce MuirBench, a comprehensive benchmark that focuses on robust
multi-image understanding capabilities of multimodal LLMs. MuirBench consists
of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that
involve 10 categories of multi-image relations (e.g., multiview, temporal
relations). Comprising 11,264 images and 2,600 multiple-choice questions,
MuirBench is created in a pairwise manner, where each standard instance is
paired with an unanswerable variant that has minimal semantic differences, in
order for a reliable assessment. Evaluated upon 20 recent multi-modal LLMs, our
results reveal that even the best-performing models like GPT-4o and Gemini Pro
find it challenging to solve MuirBench, achieving 68.0% and 49.3% in accuracy.
Open-source multimodal LLMs trained on single images can hardly generalize to
multi-image questions, hovering below 33.3% in accuracy. These results
highlight the importance of MuirBench in encouraging the community to develop
multimodal LLMs that can look beyond a single image, suggesting potential
pathways for future improvements.

MuirBench 是一个全面的基准测试，侧重于多模式 LLM 的强大的多图像理解能力。MuirBench 由 12 个不同的多图像任务（如场景理解，排序）组成，涉及 10 个多图像关系类别（如多视图关系，时间关系）。通过评估 20 种最新的多模态 LLMs，结果显示即使在表现最佳的模型 GPT-4o 和 Gemini Pro 面对 MuirBench 时也面临挑战，准确率分别为 68.0% 和 49.3%。基于单个图像训练的开源多模态 LLMs 很难推广到多图像问题，准确率低于 33.3%。这些结果凸显了 MuirBench 的重要性，鼓励社区开发能够超越单个图像的多模态 LLMs，并提出未来改进的潜在途径。