Measuring Massive Multimodal Understanding and Reasoning in Open Space

The increasing sophistication of multimodal models necessitates benchmarks that can rigorously evaluate their understanding and reasoning in complex, safety-pertinent, open-world scenarios. This study introduces M4R (Measuring Massive Multimodal Understanding and Reasoning), a large-scale benchmark uniquely designed to assess reasoning capabilities across diverse open spaces, comprehensively covering land, air, and water environments. M4R comprises approximately 2,000 videos and over 19,000 human-annotated question-answer pairs. These videos, varying in length (short, medium, long) and presenting tasks of tiered difficulty (interval-based choices and accuracy-based choices), encompass distinct operational domains: the land-based scenarios primarily focus on traffic environments, particularly traffic collisions and accident cases; the air-based scenarios center on airplane navigation; and the water-based scenarios involve ship movements. M4R systematically evaluates models on temporal-causal reasoning, spatial understanding, and intent and goal planning within these dynamic contexts. By providing a unified platform across this broad spectrum of domains, M4R aims to drive the development of more robust and generalizable AI systems. Benchmarking state-of-the-art multimodal models on our dataset reveals that even leading models, such as ChatGPT-4o and Gemini, achieve only around a 20% success rate, highlighting the significant challenges that remain in open-space multimodal reasoning.