Don't Look Only Once: Towards Multimodal Interactive Reasoning with
  Selective Visual Revisitation
Authors: name: Jiwan Chung, name: Junhyeok Kim, name: Siyeol Kim, name: Jaeyoung Lee, name: Min Soo Kim, name: Youngjae Yu

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.