VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
Researchers have introduced VAMPS, a new benchmark designed to evaluate how multimodal large language models leverage visualization tools for mathematical reasoning. The study reveals a surprising gap, showing that models still perform better with direct analytical solving than with tool-enabled visual plotting.
Computer Science > Artificial Intelligence
Title:VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
View PDF HTML (experimental)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.
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Source: arXiv cs.AI Recent
