Plug-and-play diffusion priors (PnPDP) have become a powerful paradigm for solving inverse problems in scientific and engineering domains. Yet, current evaluations of reconstruction quality emphasize point-estimate accuracy metrics on a single sample, which do not reflect the stochastic nature of PnPDP solvers and the intrinsic uncertainty of inverse problems, critical for scientific tasks. This creates a fundamental mismatch: in inverse problems, the desired output is typically a posterior distribution and most PnPDP solvers induce a distribution over reconstructions, but existing benchmarks only evaluate a single reconstruction, ignoring distributional characterization such as uncertainty. To address this gap, we conduct a systematic study to benchmark the uncertainty quantification (UQ) of existing diffusion inverse solvers. Specifically, we design a rigorous toy model simulation to evaluate the uncertainty behavior of various PnPDP solvers, and propose a UQ-driven categorization. Through extensive experiments on toy simulations and diverse real-world scientific inverse problems, we observe uncertainty behaviors consistent with our taxonomy and theoretical justification, providing new insights for evaluating and understanding the uncertainty for PnPDPs.

翻译：即插即用扩散先验（PnPDP）已成为解决科学与工程领域逆问题的强大范式。然而，当前对重建质量的评估主要关注单一样本的点估计精度指标，未能反映PnPDP求解器的随机性及逆问题固有的不确定性——这对科学任务至关重要。这导致了根本性的错配：在逆问题中，期望输出通常是后验分布，且大多数PnPDP求解器会诱导出重建结果的分布，但现有基准仅评估单一重建结果，忽略了不确定性等分布特性。为弥补这一空白，我们开展系统性研究，对现有扩散逆问题求解器的不确定性量化（UQ）进行基准测试。具体而言，我们设计了严谨的玩具模型仿真以评估各类PnPDP求解器的不确定性行为，并提出基于UQ的分类框架。通过对玩具仿真和多样化的真实科学逆问题开展大量实验，我们观察到与分类体系及理论依据一致的不确定性行为，为评估和理解PnPDP的不确定性提供了新见解。