Vision2Code is a multi-domain benchmark that evaluates image-to-code generation via rendered outputs scored by a VLM rater with dataset-specific rubrics, revealing domain-dependent model performance and enabling improvement without paired reference code.
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A literature survey synthesizing benchmarks, architectures, training strategies, and evaluation methods for mathematical reasoning in LLMs, based on roughly 120 papers.
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Vision2Code: A Multi-Domain Benchmark for Evaluating Image-to-Code Generation
Vision2Code is a multi-domain benchmark that evaluates image-to-code generation via rendered outputs scored by a VLM rater with dataset-specific rubrics, revealing domain-dependent model performance and enabling improvement without paired reference code.
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Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges
A literature survey synthesizing benchmarks, architectures, training strategies, and evaluation methods for mathematical reasoning in LLMs, based on roughly 120 papers.