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Consequently, developing a reliable benchmark that effectively evaluates large language models' (LLMs) genuine capabilities in mathematical reasoning remains a critical challenge. To address these concerns, we propose RV-Bench, a novel evaluation methodology for Benchmarking LLMs with Random Variables in mathematical reasoning. Specifically, we build question-generating functions to produce random variable questions (RVQs), whose background content mirrors original benchmark problems, but with randomized variable combinations, rendering them \"unseen\" to LLMs. Models must completely understand the inherent question pattern to correctly answer RVQs with diverse variable combinations. 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Specifically, we build question-generating functions to produce random variable questions (RVQs), whose background content mirrors original benchmark problems, but with randomized variable combinations, rendering them \"unseen\" to LLMs. Models must completely understand the inherent question pattern to correctly answer RVQs with diverse variable combinations. 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