VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos
Authors: name: Jiashuo Yu, name: Yue Wu, name: Meng Chu, name: Zhifei Ren, name: Zizheng Huang, name: Pei Chu, name: Ruijie Zhang, name: Yinan He, name: Qirui Li, name: Songze Li, name: Zhenxiang Li, name: Zhongying Tu, name: Conghui He, name: Yu Qiao, name: Yali Wang, name: Yi Wang, name: Limin Wang

We present VRBench, the first long narrative video benchmark crafted for
evaluating large models' multi-step reasoning capabilities, addressing
limitations in existing evaluations that overlook temporal reasoning and
procedural validity. It comprises 1,010 long videos (with an average duration
of 1.6 hours), along with 9,468 human-labeled multi-step question-answering
pairs and 30,292 reasoning steps with timestamps. These videos are curated via
a multi-stage filtering process including expert inter-rater reviewing to
prioritize plot coherence. We develop a human-AI collaborative framework that
generates coherent reasoning chains, each requiring multiple temporally
grounded steps, spanning seven types (e.g., event attribution, implicit
inference). VRBench designs a multi-phase evaluation pipeline that assesses
models at both the outcome and process levels. Apart from the MCQs for the
final results, we propose a progress-level LLM-guided scoring metric to
evaluate the quality of the reasoning chain from multiple dimensions
comprehensively. Through extensive evaluations of 12 LLMs and 16 VLMs on
VRBench, we undertake a thorough analysis and provide valuable insights that
advance the field of multi-step reasoning.