title: Don't Look Only Once: Towards Multimodal Interactive Reasoning with
    Selective Visual Revisitation
summary: We present v1, a lightweight extension to Multimodal Large Language Models
  (MLLMs) that enables selective visual revisitation during inference. While
  current MLLMs typically consume visual input only once and reason purely over
  internal memory, v1 introduces a simple point-and-copy mechanism that allows
  the model to dynamically retrieve relevant image regions throughout the
  reasoning process. This mechanism augments existing architectures with minimal
  modifications, enabling contextual access to visual tokens based on the model's
  evolving hypotheses. To train this capability, we construct v1g, a dataset of
  300K multimodal reasoning traces with interleaved visual grounding annotations.
  Experiments on three multimodal mathematical reasoning benchmarks -- MathVista,
  MathVision, and MathVerse -- demonstrate that v1 consistently improves
  performance over comparable baselines, particularly on tasks requiring
  fine-grained visual reference and multi-step reasoning. Our results suggest
  that dynamic visual access is a promising direction for enhancing grounded
  multimodal reasoning. Code, models, and data will be released to support future
  research.
paper_authors._id: 6839543d6451d371f9e834ec
paper_authors.name: Jiwan Chung
paper_authors.status: claimed_verified
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paper_authors.fullname: Jiwan Chung
paper_authors.type: user
paper_authors.user: jiwan-chung
paper_authors._id: 6839543d6451d371f9e834ed
paper_authors.name: Junhyeok Kim
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paper_authors.fullname: Junhyeok Kim
paper_authors.type: user
paper_authors.user: kjunh
paper_authors._id: 6839543d6451d371f9e834ee
paper_authors.name: Siyeol Kim
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paper_authors.fullname: Siyeol Kim
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paper_authors.user: siyeolkim
paper_authors._id: 6839543d6451d371f9e834ef
paper_authors.name: Jaeyoung Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T13:39:57.765Z
paper_authors._id: 64ca095f29d2f65419015fcb
paper_authors.avatarUrl: /avatars/722df32d9d99382fdc038ca8f1c15c65.svg
paper_authors.fullname: Jaeyoung Lee
paper_authors.type: user
paper_authors.user: given131
paper_authors._id: 6839543d6451d371f9e834f0
paper_authors.name: Min Soo Kim
paper_authors._id: 6839543d6451d371f9e834f1
paper_authors.name: Youngjae Yu
paper_title: Don't Look Only Once: Towards Multimodal Interactive Reasoning with
    Selective Visual Revisitation
paper_summary: We present v1, a lightweight extension to Multimodal Large Language Models
  (MLLMs) that enables selective visual revisitation during inference. While
  current MLLMs typically consume visual input only once and reason purely over
  internal memory, v1 introduces a simple point-and-copy mechanism that allows
  the model to dynamically retrieve relevant image regions throughout the
  reasoning process. This mechanism augments existing architectures with minimal
  modifications, enabling contextual access to visual tokens based on the model's
  evolving hypotheses. To train this capability, we construct v1g, a dataset of
  300K multimodal reasoning traces with interleaved visual grounding annotations.
  Experiments on three multimodal mathematical reasoning benchmarks -- MathVista,
  MathVision, and MathVerse -- demonstrate that v1 consistently improves
  performance over comparable baselines, particularly on tasks requiring
  fine-grained visual reference and multi-step reasoning. Our results suggest
  that dynamic visual access is a promising direction for enhancing grounded
  multimodal reasoning. Code, models, and data will be released to support future
  research.
paper_ai_summary: v1 enhances Multimodal Large Language Models by enabling selective and dynamic visual region retrieval during inference, improving performance on multimodal reasoning tasks.
paper_ai_keywords: Multimodal Large Language Models (MLLMs) point-and-copy mechanism visual tokens multimodal reasoning traces visual grounding annotations MathVista MathVision MathVerse grounded multimodal reasoning