做条件生成或科学模拟的团队终于有了一个靠谱的评估工具——不用依赖未知的目标分布，直接用训练数据就能判断生成样本质量，建议做生物成像或可控生成的开发者试试。
该研究提出了一种后验的、每个样本的信任分数，用于评估条件生成模型在组合偏移下的样本质量。标准评估指标需要参考目标分布，但在外推场景中该分布不可用。新方法结合全局真实性和属性忠实度两个可估计量，仅使用训练分布即可评估样本。实验表明，该方法能有效过滤、排序和弃权生成样本，在生物成像和视觉基准测试中提升了下游性能。代码已开源。
Assessing Sample Quality in Conditional Generation under Compositional Shift
Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimental factors. In many applications, especially in scientific domains, such models are attractive to explore conditions for which real samples are rare, expensive, or not yet observed. However, this creates a circularity for evaluation: standard conditional quality metrics require a reference target distribution, but in the extrapolative regime that distribution is unavailable by definition. We address this problem with a post-hoc, per-sample trust score for assessing conditional samples using only the training distribution. The score combines two estimable quantities: global realism, measuring compatibility with the real data manifold, and attribute-wise faithfulness, measuring whether a sample is closer to the requested attributes than to plausible alternatives. We show that the score can recover meaningful comparisons across extrapolated generations, under a mild coverage condition on the observed attributes. These comparisons enable effective filtering, ranking, and abstention of generations and can be used directly on off-the-shelf pretrained models. In biological imaging, selected samples preserve real morphological structure better and improve downstream predictive performance, while similar gains are observed on controlled vision benchmarks. Finally, we show how the score can be applied during generation, enabling abstention before full decoding. Code is available at https://github.com/berkerdemirel/faithful-cond-gen.