Benchmarking Scientific Understanding and Reasoning for Video Generation using VideoScience-Bench

The next frontier for video generation lies in developing models capable of zero-shot reasoning, where understanding real-world scientific laws is crucial for accurate modeling of physical outcomes under diverse conditions. However, existing video benchmarks are physical commonsense-based, offering limited insight into video models’ scientific reasoning capability. We introduce VideoScience-Bench1, a benchmark for evaluating undergraduate-level scientific understanding in video models. Each prompt encodes a composite scientific scenario that requires understanding and reasoning across multiple scientific concepts to generate the correct phenomenon. The benchmark comprises 200 evaluation items built from 160 expert-curated test cases (160 text-to-video prompts; 40 also include image conditioning) spanning 14 topics and 103 concepts. We conduct expert-annotated evaluations across seven state-of-the-art video models in T2V and I2V settings, along five dimensions: Prompt Consistency, Phenomenon Congruency, Dynamism, Immutability, and Spatio-Temporal Coherence. Using a VLM-as-a-Judge to assess video generations, we observe strong correlation with human assessments. To the best of our knowledge, VideoScience-Bench is the first benchmark to evaluate video models not only as generators but also as reasoners, requiring their generations to demonstrate scientific understanding consistent with expected physical and chemical phenomena.