理解与生成需要两套数据标准：CAPEval揭开Caption质量密码

TL;DR - CAPEval is a caption-evaluation framework from UCAS that splits caption quality into Coverage (how much of the image's facts are stated) and Precision (how much of what's stated is correct), instead of collapsing them into one score. It matters because the two dimensions predict downstream success differently: understanding models want coverage, text-to-image models want precision.

• Benchmark construction: human-written ground-truth captions per image are decomposed into an atomic checklist of independently verifiable facts; a judge scores a candidate caption against the checklist without seeing the image, marking each fact correct / wrong / unmentioned.
• Controlled downstream study: captions from 10 caption models were used as the only varying factor in otherwise identical VLM and text-to-image training pipelines, isolating the effect of caption supervision.
• Regression over all 10 models found Coverage is the stable, statistically significant predictor for vision-language understanding, while Precision is the stable predictor for text-to-image generation — wrong facts inject bad conditioning signal, which hurts generation more than missing facts.
• Counterintuitive result within InternVL3.5: the 1B captioner beat the 8B for understanding (higher Coverage) and the 4B beat the 8B for generation (higher Precision), so captioner parameter count is not a proxy for data-generator value; the paper argues for task-specific "dual-track" caption data.