MathVis-Fine: Progressive Dependency-Guided Training for Math

MathVis-Fine, a dataset and training framework for multimodal mathematical reasoning from Wanshi Xu, Haokun Zhao, Haidong Yuan, Songjun Cao and Long Ma, was submitted to arXiv on 16 Jun 2026 as arXiv:2606.17888. The paper introduces a MathVis-Fine dataset with fine-grained visual annotations and visual dependency ratings, and a two-stage progressive visual enhancement training paradigm that adjusts visual supervision according to each sample's visual dependency.

What is MathVis-Fine?

MathVis-Fine is both a dataset and a training framework that models fine-grained visual dependencies in mathematical problem solving. The authors say they constructed the MathVis-Fine dataset by augmenting problems with fine-grained visual annotations plus explicit visual dependency ratings. The paper also states, "We will release the dataset upon acceptance."

The dataset is presented as a core enabler for the method: the visual dependency ratings are intended to indicate, per sample, how necessary visual information is for reaching the correct answer. The authors position these ratings as a way to avoid treating visual inputs as homogeneous or merely auxiliary signals.

How does the progressive dependency-guided training work?

The framework uses a two-stage progressive visual enhancement training paradigm that balances answer correctness rewards and visual grounding rewards based on the intrinsic visual dependency level of each sample. In short: the model receives different mixes of rewards depending on how dependent a problem is on its visual content, which the authors say mitigates reward bias and improves supervision accuracy.

Stage one and stage two are described as a progressive scheme: visual perception is enhanced gradually according to the sample-level visual dependency signals drawn from the MathVis-Fine annotations. The paper frames the approach as addressing two core issues with prior multimodal Chain-of-Thought work: supervisory signals for visual content being coarse-grained and training feedback becoming inaccurate when visual rewards are applied uniformly across samples.

What evidence do the authors provide?

The authors report running "extensive experiments" and state that the MathVis-Fine framework "effectively enhances visual perception progressively based on visual dependency." The abstract claims the training paradigm balances answer correctness rewards and visual grounding rewards according to each sample's visual dependency level, and that this mitigates reward bias and improves supervision accuracy. The submission does not embed numeric benchmark scores or dataset sizes in the abstract metadata on arXiv, but it does provide the paper's identifier arXiv:2606.17888 and the submission date, 16 Jun 2026.

Why it matters

Multimodal mathematical reasoning tasks mix text and images in ways that are sample dependent: some problems need the visual diagram to solve them, others do not. MathVis-Fine targets that heterogeneity directly by converting necessity into a training signal, rather than treating all visual data the same. If the dataset and the dependency-guided reward scheme perform as the authors claim in their experiments, this could reduce spurious grounding and make visual supervision more targeted, benefiting models trained on mixed visual necessity workloads.

What to watch

Look for the public dataset release after the paper's acceptance and for the experimental section in the full paper to include concrete metrics and dataset counts. Those details will show whether the dependency ratings and the two-stage reward balancing produce consistent gains across problem types and difficulty levels.