title: VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video
    Reasoning?
summary: Recent studies have shown that long chain-of-thought (CoT) reasoning can
  significantly enhance the performance of large language models (LLMs) on
  complex tasks. However, this benefit is yet to be demonstrated in the domain of
  video understanding, since most existing benchmarks lack the reasoning depth
  required to demonstrate the advantages of extended CoT chains. While recent
  efforts have proposed benchmarks aimed at video reasoning, the tasks are often
  knowledge-driven and do not rely heavily on visual content. To bridge this gap,
  we introduce VideoReasonBench, a benchmark designed to evaluate vision-centric,
  complex video reasoning. To ensure visual richness and high reasoning
  complexity, each video in VideoReasonBench depicts a sequence of fine-grained
  operations on a latent state that is only visible in part of the video. The
  questions evaluate three escalating levels of video reasoning skills: recalling
  observed visual information, inferring the content of latent states, and
  predicting information beyond the video. Under such task setting, models have
  to precisely recall multiple operations in the video, and perform step-by-step
  reasoning to get correct final answers for these questions. Using
  VideoReasonBench, we comprehensively evaluate 18 state-of-the-art multimodal
  LLMs (MLLMs), finding that most perform poorly on complex video reasoning,
  e.g., GPT-4o achieves only 6.9% accuracy, while the thinking-enhanced
  Gemini-2.5-Pro significantly outperforms others with 56.0% accuracy. Our
  investigations on "test-time scaling" further reveal that extended thinking
  budget, while offering none or minimal benefits on existing video benchmarks,
  is essential for improving the performance on VideoReasonBench.
paper_authors._id: 683909a29deef11aa625817c
paper_authors.name: Yuanxin Liu
paper_authors._id: 683909a29deef11aa625817d
paper_authors.name: Kun Ouyang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:54:22.290Z
paper_authors._id: 62cd7aca7a036fc9941bb2b0
paper_authors.avatarUrl: /avatars/17a4d27af0243fd7dccf06066f671461.svg
paper_authors.fullname: kun ouyang
paper_authors.type: user
paper_authors.user: RUBBISHLIKE
paper_authors._id: 683909a29deef11aa625817e
paper_authors.name: Haoning Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:47:29.111Z
paper_authors._id: 63047ed2412a1b9d381b09c9
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paper_authors.fullname: Haoning Wu, Teo
paper_authors.type: user
paper_authors.user: teowu
paper_authors._id: 683909a29deef11aa625817f
paper_authors.name: Yi Liu
paper_authors._id: 683909a29deef11aa6258180
paper_authors.name: Lin Sui
paper_authors._id: 683909a29deef11aa6258181
paper_authors.name: Xinhao Li
paper_authors._id: 683909a29deef11aa6258182
paper_authors.name: Yan Zhong
paper_authors._id: 683909a29deef11aa6258183
paper_authors.name: Y. Charles
paper_authors._id: 683909a29deef11aa6258184
paper_authors.name: Xinyu Zhou
paper_authors._id: 683909a29deef11aa6258185
paper_authors.name: Xu Sun
paper_title: VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video
    Reasoning?
paper_summary: Recent studies have shown that long chain-of-thought (CoT) reasoning can
  significantly enhance the performance of large language models (LLMs) on
  complex tasks. However, this benefit is yet to be demonstrated in the domain of
  video understanding, since most existing benchmarks lack the reasoning depth
  required to demonstrate the advantages of extended CoT chains. While recent
  efforts have proposed benchmarks aimed at video reasoning, the tasks are often
  knowledge-driven and do not rely heavily on visual content. To bridge this gap,
  we introduce VideoReasonBench, a benchmark designed to evaluate vision-centric,
  complex video reasoning. To ensure visual richness and high reasoning
  complexity, each video in VideoReasonBench depicts a sequence of fine-grained
  operations on a latent state that is only visible in part of the video. The
  questions evaluate three escalating levels of video reasoning skills: recalling
  observed visual information, inferring the content of latent states, and
  predicting information beyond the video. Under such task setting, models have
  to precisely recall multiple operations in the video, and perform step-by-step
  reasoning to get correct final answers for these questions. Using
  VideoReasonBench, we comprehensively evaluate 18 state-of-the-art multimodal
  LLMs (MLLMs), finding that most perform poorly on complex video reasoning,
  e.g., GPT-4o achieves only 6.9% accuracy, while the thinking-enhanced
  Gemini-2.5-Pro significantly outperforms others with 56.0% accuracy. Our
  investigations on "test-time scaling" further reveal that extended thinking
  budget, while offering none or minimal benefits on existing video benchmarks,
  is essential for improving the performance on VideoReasonBench.
paper_ai_summary: A new benchmark, VideoReasonBench, evaluates complex vision-centric video reasoning, finding that extended thinking budgets are crucial for improved performance compared to existing benchmarks.
paper_ai_keywords: long chain-of-thought reasoning large language models video understanding VideoReasonBench vision-centric complex video reasoning latent state visual reasoning step-by-step reasoning multimodal language models thinking-enhanced models test-time scaling