SciVideoBench: Benchmarking Scientific Video Reasoning in Large
  Multimodal Models
Authors: name: Andong Deng, name: Taojiannan Yang, name: Shoubin Yu, name: Lincoln Spencer, name: Mohit Bansal, name: Chen Chen, name: Serena Yeung-Levy, name: Xiaohan Wang

Large Multimodal Models (LMMs) have achieved remarkable progress across
various capabilities; however, complex video reasoning in the scientific domain
remains a significant and challenging frontier. Current video benchmarks
predominantly target general scenarios where perception/recognition is heavily
relied on, while with relatively simple reasoning tasks, leading to saturation
and thus failing to effectively evaluate advanced multimodal cognitive skills.
To address this critical gap, we introduce SciVideoBench, a rigorous benchmark
specifically designed to assess advanced video reasoning in scientific
contexts. SciVideoBench consists of 1,000 carefully crafted multiple-choice
questions derived from cutting-edge scientific experimental videos spanning
over 25 specialized academic subjects and verified by a semi-automatic system.
Each question demands sophisticated domain-specific knowledge, precise
spatiotemporal perception, and intricate logical reasoning, effectively
challenging models' higher-order cognitive abilities. Our evaluation highlights
significant performance deficits in state-of-the-art proprietary and
open-source LMMs, including Gemini 2.5 Pro and Qwen2.5-VL, indicating
substantial room for advancement in video reasoning capabilities. Detailed
analyses of critical factors such as reasoning complexity and visual grounding
provide valuable insights and clear direction for future developments in LMMs,
driving the evolution of truly capable multimodal AI co-scientists. We hope
SciVideoBench could fit the interests of the community and help to push the
boundary of cutting-edge AI for border science.