v1: Learning to Point Visual Tokens for Multimodal Grounded Reasoning

Triangle RST on a patch grid. Side RS is labelled 2z minus 15, side RT is 9, side ST is 7. The angles at S and T carry matching marks. — Most multimodal language models encode an image once, then reason purely in text. v1 keeps looking: it points at the patches that matter and copies them back into its chain of thought. Press play, or hover a copied patch to see where it came from. Static figure

point-and-copy

Reasoning that revisits the image

A real trace from the v1g training set. As the model works through the problem, each detect(…) call grounds the next step in a region of the image. Hover or focus an ⟨obj⟩ token to see where it points.

task AB is tangent to circle O at A, and ∠AOB = 70°. Find ∠B. A: 10° B: 20° C: 30° D: 35°

I know that a tangent to a circle is perpendicular to the radius at the point of tangency. So, I need to look at the tangent line AB.

detect(query="tangent line AB", objects=[])

Since is tangent to the circle at point A, then OA, which is the radius, should be perpendicular to it. Let me find radius OA.

detect(query="radius OA", objects=[])

That means angle OAB is 90 degrees.

detect(query="angle OAB", objects=[])
detect(query="angle AOB", objects=[])
detect(query="triangle AOB", objects=[])

In a right triangle , the sum of the angles is 180 degrees. So, angle B = 180° − − = 180° − 90° − 70° = 20°. ✓ B

abstract

Why point-and-copy

When thinking with images, humans rarely rely on a single glance: they revisit visual evidence while reasoning. In contrast, most Multimodal Language Models encode an image once to key-value cache and then reason purely in text, making it hard to re-ground intermediate steps. We empirically confirm this: as reasoning chains lengthen, models progressively lose focus on relevant regions. We introduce v1, a lightweight extension for active visual referencing via point-and-copy: the model selects relevant image patches and copies their embeddings back into the reasoning stream. Crucially, our point-and-copy mechanism retrieves patches using their semantic representations as keys, ensuring perceptual evidence remains aligned with the reasoning space. To train this behavior, we build v1g, a dataset of 300K multimodal reasoning traces with interleaved grounding annotations. Across multimodal mathematical reasoning benchmarks, v1 consistently outperforms comparable baselines.

artifacts

Everything is released

model kjunh/v1-7B v1 on Qwen2.5-VL-7B, ready for inference and the Gradio demo. dataset kjunh/v1g 300K reasoning traces with interleaved grounding annotations. code jun297/v1 Inference, Gradio demo, and full training code.

cite

BibTeX

@misc{chung2025v1learningpointvisual,
  title={v1: Learning to Point Visual Tokens for Multimodal Grounded Reasoning},
  author={Jiwan Chung and Junhyeok Kim and Siyeol Kim and Jaeyoung Lee
          and Min Soo Kim and Youngjae Yu},
  year={2025},
  eprint={2505.18842},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2505.18842},
}

Learning to Point Visual Tokens
for Multimodal Grounded Reasoning

Standard approach text-based reasoning

v1 point-and-copy reasoning