Don't Look Only Once: Towards Multimodal Interactive Reasoning with
  Selective Visual Revisitation
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.
6839543d6451d371f9e834ec
Jiwan Chung
claimed_verified
2025-06-03T08:50:27.211Z
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/avatars/9b1a2df9402e9c26e1eb7c818af9bae0.svg
Jiwan Chung
user
jiwan-chung
6839543d6451d371f9e834ed
Junhyeok Kim
claimed_verified
2025-06-02T07:46:37.442Z
646aecb04c1cd18b497a50ee
/avatars/de15c724056f36a41cb4f375d05ed836.svg
Junhyeok Kim
user
kjunh
6839543d6451d371f9e834ee
Siyeol Kim
claimed_verified
2025-06-02T07:46:34.334Z
67021743e4d49b157afd8260
/avatars/2a22a18cd45f6d115e8a3a5d1e477dcb.svg
Siyeol Kim
user
siyeolkim
6839543d6451d371f9e834ef
Jaeyoung Lee
claimed_verified
2025-06-02T13:39:57.765Z
64ca095f29d2f65419015fcb
/avatars/722df32d9d99382fdc038ca8f1c15c65.svg
Jaeyoung Lee
user
given131
6839543d6451d371f9e834f0
Min Soo Kim
6839543d6451d371f9e834f1
Youngjae Yu
Don't Look Only Once: Towards Multimodal Interactive Reasoning with
  Selective Visual Revisitation
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.
v1 enhances Multimodal Large Language Models by enabling selective and dynamic visual region retrieval during inference, improving performance on multimodal reasoning tasks.
Multimodal Large Language Models (MLLMs) point-and-copy mechanism visual tokens multimodal reasoning traces visual grounding annotations MathVista MathVision MathVerse grounded multimodal reasoning