title: Benchmarking Multimodal Mathematical Reasoning with Explicit Visual
    Dependency
summary: Recent advancements in Large Vision-Language Models (LVLMs) have
  significantly enhanced their ability to integrate visual and linguistic
  information, achieving near-human proficiency in tasks like object recognition,
  captioning, and visual question answering. However, current benchmarks
  typically focus on knowledge-centric evaluations that assess domain-specific
  expertise, often neglecting the core ability to reason about fundamental
  mathematical elements and visual concepts. We identify a gap in evaluating
  elementary-level math problems, which rely on explicit visual
  dependencies-requiring models to discern, integrate, and reason across multiple
  images while incorporating commonsense knowledge, all of which are crucial for
  advancing toward broader AGI capabilities. To address this gap, we introduce
  VCBENCH, a comprehensive benchmark for multimodal mathematical reasoning with
  explicit visual dependencies. VCBENCH includes 1,720 problems across six
  cognitive domains, featuring 6,697 images (averaging 3.9 per question) to
  ensure multi-image reasoning. We evaluate 26 state-of-the-art LVLMs on VCBENCH,
  revealing substantial performance disparities, with even the top models unable
  to exceed 50% accuracy. Our findings highlight the ongoing challenges in
  visual-mathematical integration and suggest avenues for future LVLM
  advancements.
paper_authors._id: 68106d0daa36fca4aeebb34a
paper_authors.name: Zhikai Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-29T07:57:55.121Z
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paper_authors.avatarUrl: /avatars/cab345716718965b87e29ac248fbc7e4.svg
paper_authors.fullname: zhikai wang
paper_authors.type: user
paper_authors.user: cloudcatcher2
paper_authors._id: 68106d0daa36fca4aeebb34b
paper_authors.name: Jiashuo Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:25:04.139Z
paper_authors._id: 64527f701a57e1179c1c3693
paper_authors.avatarUrl: /avatars/25b2632d7aa9ce26d5d4924ecb00c4f4.svg
paper_authors.fullname: Jiashuo Sun
paper_authors.type: user
paper_authors.user: gasolsun
paper_authors._id: 68106d0daa36fca4aeebb34c
paper_authors.name: Wenqi Zhang
paper_authors._id: 68106d0daa36fca4aeebb34d
paper_authors.name: Zhiqiang Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:25:37.087Z
paper_authors._id: 637f228152229c63921119c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/637f228152229c63921119c3/acwXorra1r9_7i3KlBFjS.jpeg
paper_authors.fullname: Zhiqiang Hu
paper_authors.type: user
paper_authors.user: Zhiqiang007
paper_authors._id: 68106d0daa36fca4aeebb34e
paper_authors.name: Xin Li
paper_authors._id: 68106d0daa36fca4aeebb34f
paper_authors.name: Fan Wang
paper_authors._id: 68106d0daa36fca4aeebb350
paper_authors.name: Deli Zhao
paper_title: Benchmarking Multimodal Mathematical Reasoning with Explicit Visual
    Dependency
paper_summary: Recent advancements in Large Vision-Language Models (LVLMs) have
  significantly enhanced their ability to integrate visual and linguistic
  information, achieving near-human proficiency in tasks like object recognition,
  captioning, and visual question answering. However, current benchmarks
  typically focus on knowledge-centric evaluations that assess domain-specific
  expertise, often neglecting the core ability to reason about fundamental
  mathematical elements and visual concepts. We identify a gap in evaluating
  elementary-level math problems, which rely on explicit visual
  dependencies-requiring models to discern, integrate, and reason across multiple
  images while incorporating commonsense knowledge, all of which are crucial for
  advancing toward broader AGI capabilities. To address this gap, we introduce
  VCBENCH, a comprehensive benchmark for multimodal mathematical reasoning with
  explicit visual dependencies. VCBENCH includes 1,720 problems across six
  cognitive domains, featuring 6,697 images (averaging 3.9 per question) to
  ensure multi-image reasoning. We evaluate 26 state-of-the-art LVLMs on VCBENCH,
  revealing substantial performance disparities, with even the top models unable
  to exceed 50% accuracy. Our findings highlight the ongoing challenges in
  visual-mathematical integration and suggest avenues for future LVLM
  advancements.
paper_ai_summary: VCBENCH evaluates multimodal mathematical reasoning by assessing LVLMs on a benchmark with explicit visual dependencies and multi-image reasoning tasks.
paper_ai_keywords: Large Vision-Language Models LVLMs multimodal mathematical reasoning explicit visual dependencies