arXiv Machine Learning October 12, 2026 By Ajay Vikram Periasami, Junlin Wang, Bhuwan Dhingra

Vision2Code: A Multi-Domain Benchmark for Evaluating Image-to-Code Generation

Read the original on arXiv Machine Learning →

Vision2Code is a new benchmark for image-to-code generation that covers 15 domains and 539 test examples without relying on reference source code. It evaluates generated programs by executing them and comparing rendered outputs to the original images using domain‑specific rubrics, yielding scores that correlate better with human judgments than generic metrics. The study shows domain variability in model performance and demonstrates that the scoring method can improve fine‑tuning of models like Qwen3.5‑9B and Phi‑4‑MM via rejection sampling.

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