This research introduces a comprehensive benchmark for evaluating large vision-language models on interleaved multimodal comprehension tasks across diverse domains.

• Addresses critical gaps in existing evaluation frameworks for multimodal AI
• Spans multiple knowledge domains including education, art, linguistics, and medicine
• Offers more reliable metrics for assessing how AI systems process and generate mixed text-image content
• Provides deeper evaluation depth than current benchmarks

For educators, this benchmark enables more accurate assessment of AI systems' capabilities to understand complex educational content involving both text and visuals—crucial for developing AI tools that can effectively support teaching and learning across subjects like mathematics, coding, and literature.

MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models