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For open-ended generation there's no ground-truth string to match, so accuracy is meaningless. The field relies on a layered mix of automatic, model-based, and human metrics. Here is how to assemble a credible eval.

An eval suite that never varies the image will happily pass a model that has stopped looking at it. Start with the modality ablation, then pick metrics by what the system actually produces. Here is the full stack.