VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then reason over the tool's output, specifically when relying on visual aids. To study this discrepancy, we introduce VAMPS (Visual-Assisted Mathematical Problem Solving), a benchmark for graph-assisted mathematics. VAMPS contains 1,168 multimodal, bilingual multiple-choice question-answer pairs drawn from Iranian University Entrance Exam algebra and calculus problems, expanded with human-reviewed LLM-generated synthetic variants. Overall, we found that direct analytical solving surprisingly outperforms tool-enabled visual solving, even on problems where plotting is a natural strategy.
[2606.04244] VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
[Submitted on 2 Jun 2026]
Title:VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
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Abstract:Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then reason over the tool's output, specifically when they rely on visual aids. This gap is especially important because real engineering and scientific workflows often rely on visualization tools for analysis, validation, and decision-making. To study this discrepancy, we introduce VAMPS (Visual-Assisted Mathematical Problem Solving), a benchmark for graph-assisted mathematics. VAMPS contains 1,168 multimodal, bilingual multiple-choice question-answer pairs drawn from Iranian University Entrance Exam algebra and calculus problems and expanded with human-reviewed LLM-generated synthetic variants, all selected so that plotting provides a natural solution strategy by revealing intersections, extrema, asymptotes, etc. Designed for both benchmarking and diagnosis, VAMPS goes beyond prior multimodal benchmarks that primarily evaluate reasoning over fixed visual inputs by testing whether a model can benefit from constructing a useful graph and grounding its answer in the resulting visualization. Overall, we found that across a diverse set of models, direct analytical solving surprisingly outperforms tool-enabled visual solving, even on problems where plotting is a natural strategy.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2606.04244 [cs.AI]
(or arXiv:2606.04244v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.04244
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Amirhossein Dabiriaghdam [view email] [v1] Tue, 2 Jun 2026 21:45:21 UTC (2,354 KB)
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