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arxiv: 2402.14804 · v1 · pith:JATB3AB7new · submitted 2024-02-22 · 💻 cs.CV · cs.AI· cs.CL· cs.LG· math.HO

Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset

Ke Wang , Junting Pan , Weikang Shi , Zimu Lu , Mingjie Zhan , Hongsheng Li This is my paper

Pith reviewed 2026-05-17 20:40 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.CLcs.LGmath.HO
keywords MATH-Visionlarge multimodal modelsvisual mathematical reasoningbenchmark datasetmath competitionsperformance gaperror analysismultimodal evaluation
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The pith

The MATH-Vision dataset of 3,040 competition-sourced visual math problems reveals a large performance gap between current large multimodal models and human solvers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the MATH-Vision dataset to overcome limitations in existing benchmarks for evaluating large multimodal models on mathematical reasoning with visual elements. It compiles 3,040 problems from real competitions that cover 16 different mathematical subjects and range across five difficulty levels. Experiments on this dataset show that current models fall substantially short of human performance levels. The structured categories in the dataset also support detailed breakdowns of where models make errors. These results point to the need for targeted progress in handling diverse visual math challenges.

Core claim

The authors present the MATH-Vision dataset, a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions and spanning 16 distinct disciplines graded across five levels of difficulty, and demonstrate through extensive testing that current large multimodal models exhibit a notable performance gap relative to human performance on this benchmark.

What carries the argument

The MATH-Vision dataset, which functions as a diverse evaluation benchmark by supplying categorized visual math problems drawn directly from competitions to measure model reasoning capabilities.

If this is right

  • Existing large multimodal models must advance substantially to match human performance on visual mathematical tasks drawn from competitions.
  • The dataset's subject and difficulty categorizations enable precise identification of specific failure modes in model reasoning.
  • Future model development should prioritize coverage of a wider range of mathematical disciplines and visual problem formats.
  • Benchmarks like MATH-Vision set a higher standard than prior sets such as MathVista for assessing multimodal mathematical abilities.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Models that close the gap on this dataset may also handle real-world visual problem solving that involves diagrams and charts more effectively.
  • The error analysis categories could guide the creation of targeted training examples to address common model weaknesses.
  • Extending similar curation methods to additional languages or problem sources might further strengthen evaluation of multimodal reasoning.

Load-bearing premise

The selected competition problems form a representative and unbiased sample of visual mathematical reasoning challenges without favoring particular problem types or introducing curation biases.

What would settle it

A new large multimodal model that reaches or exceeds average human accuracy on the full MATH-Vision set while maintaining similar performance on held-out competition problems would falsify the claimed persistent gap.

read the original abstract

Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address this issue, we present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. Spanning 16 distinct mathematical disciplines and graded across 5 levels of difficulty, our dataset provides a comprehensive and diverse set of challenges for evaluating the mathematical reasoning abilities of LMMs. Through extensive experimentation, we unveil a notable performance gap between current LMMs and human performance on MATH-V, underscoring the imperative for further advancements in LMMs. Moreover, our detailed categorization allows for a thorough error analysis of LMMs, offering valuable insights to guide future research and development. The project is available at https://mathvision-cuhk.github.io

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces the MATH-Vision (MATH-V) dataset: a collection of 3,040 visual mathematical problems curated from real competitions, spanning 16 disciplines and 5 difficulty levels. It evaluates multiple large multimodal models (LMMs) on this benchmark, reports a substantial performance gap relative to human solvers, and provides a categorized error analysis to identify specific weaknesses in current LMMs.

Significance. A larger, more diverse benchmark drawn from authentic competition sources would be a useful addition to the multimodal reasoning literature if the reported gap is robust. The fine-grained categorization by discipline and difficulty enables targeted diagnostics that could usefully guide model development beyond aggregate accuracy numbers.

major comments (2)
  1. [Dataset curation] Dataset curation section: the manuscript states that the 3,040 problems were 'meticulously curated' from real competitions but supplies no quantitative comparison (e.g., histograms or tables) of discipline frequencies, diagram density, or difficulty distribution between the selected subset and the full source pools. Without this, it is impossible to rule out selection effects that could inflate the observed LMM-human gap.
  2. [Experiments] Experiments / evaluation protocol: the paper does not specify the exact prompting templates, few-shot examples, decoding parameters, or answer-extraction rules used for each LMM. These details are load-bearing for reproducing the reported accuracies and for confirming that the performance gap is not an artifact of evaluation choices.
minor comments (2)
  1. [Abstract] The abstract and introduction could state the number of LMMs evaluated and the headline accuracy numbers to give readers an immediate quantitative sense of the gap.
  2. [Figures and Tables] Figure captions and table headers should explicitly note the evaluation metric (e.g., exact-match accuracy) and any human baseline collection details.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important aspects of transparency in dataset curation and reproducibility in evaluation. We address each major comment below and will revise the manuscript accordingly to strengthen these elements.

read point-by-point responses

  1. Referee: [Dataset curation] Dataset curation section: the manuscript states that the 3,040 problems were 'meticulously curated' from real competitions but supplies no quantitative comparison (e.g., histograms or tables) of discipline frequencies, diagram density, or difficulty distribution between the selected subset and the full source pools. Without this, it is impossible to rule out selection effects that could inflate the observed LMM-human gap.

    Authors: We agree that quantitative comparisons would improve transparency and help address potential concerns about selection bias. Our curation process prioritized diversity across 16 disciplines and 5 difficulty levels from authentic competition sources. In the revised manuscript, we will add histograms, tables, and statistics comparing discipline frequencies, difficulty distributions, and diagram densities in MATH-V to available aggregate data from the source competitions. This will better demonstrate representativeness of the selected subset. revision: yes

  2. Referee: [Experiments] Experiments / evaluation protocol: the paper does not specify the exact prompting templates, few-shot examples, decoding parameters, or answer-extraction rules used for each LMM. These details are load-bearing for reproducing the reported accuracies and for confirming that the performance gap is not an artifact of evaluation choices.

    Authors: We acknowledge that detailed evaluation protocols are essential for reproducibility. We will revise the Experiments section and include a new appendix specifying the exact prompting templates, few-shot examples (where used), decoding parameters such as temperature and top-p, and answer-extraction rules for each LMM. These additions will enable full replication of the reported results and confirm the robustness of the performance gap. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation or claims

full rationale

The paper introduces the MATH-V dataset by curating 3,040 problems from real competitions across 16 disciplines and 5 difficulty levels, then reports empirical accuracy results for LMMs versus humans using standard metrics. No equations, parameter fits, self-definitions, or derivations are present that reduce any claim to its own inputs by construction. The performance gap is measured directly on the new external data rather than being statistically forced or renamed from prior results. Self-citations are not load-bearing for the central claim, and the evaluation remains self-contained against external benchmarks without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced; the work rests on standard dataset curation and empirical evaluation practices from the multimodal AI literature.

pith-pipeline@v0.9.0 · 5501 in / 976 out tokens · 29825 ms · 2026-05-17T20:40:56.021007+00:00 · methodology

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Reference graph

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    Transform the top right vertex (2, 2): 1 2 , 1 2 = 1 2 , 1 2 . This point moves closer to both axes. After applying the transformation to all four vertices of the square, we can see that the new shape is also a square, but it is smaller and has its bottom left vertex at 1 2 , 1 2 and its top right vertex at (1, 1). This matches the description of diagram ...

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