发表机构
The Hong Kong University of Science and Technology; The Chinese University of Hong Kong, Shenzhen（香港科技大学; 香港中文大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GPARA通过图代理学习扩散预测残差，构建可复用的后验响应算子，实现主动采集与重建精化，在物理场和视觉任务上超越基线。
AI 中文摘要
主动接地一个冻结的扩散先验需要联合确定应在何处采集新测量数据以及如何利用这些数据来精化当前重建。基于后验集成的方法可以从生成的样本中估计采集效用，但随着观测数据的累积，这些方法需要重复生成集成，并且仅通过经验统计量捕捉后验几何结构。本文提出GPARA，该方法在扩散预测残差上学习一个上下文相关的图代理，诱导出一个显式可复用的后验响应算子，该算子将测量创新传播到未观测变量，并通过加权后验风险降低来评估候选测量。在匹配的代理下，我们证明同一响应算子也决定了期望的一步采集收益，并产生与期望重建改进一致的解析排序。一个有界的习得残差校准解析效用以考虑代理失配，而一个小型先验集成仅生成一次，并通过重新条件化来更新风险权重，而无需在采集过程中重复进行扩散后验采样。在跨越物理场和计算机视觉的两个重建任务上的实验表明，在精化和主动采集方面相对于评估基线有一致的改进。消融研究进一步支持逐步图精化、自适应风险加权和解析锚定校准的互补作用。
英文摘要
Active grounding of a frozen diffusion prior requires jointly determining where new measurements should be taken and how they should be used to refine the current reconstruction. Posterior-ensemble-based methods can estimate acquisition utility from generated samples, but require repeated ensemble generation as observations accumulate and capture posterior geometry only through empirical statistics. This paper proposes GPARA, which learns a context-dependent graph surrogate over diffusion prediction residuals, inducing an explicitly reusable posterior response operator that propagates measurement innovations to unobserved variables and evaluates candidate measurements through weighted posterior-risk reduction. Under the matched surrogate, we show that the same response operator also determines expected one-step acquisition benefit and yields an analytic ranking consistent with expected reconstruction improvement. A bounded learned residual calibrates the analytic utility to account for surrogate mismatch, while a small prior ensemble is generated once and reconditioned to update risk weights without repeated diffusion posterior sampling during acquisition. Experiments on two reconstruction tasks spanning physical field and computer vision show consistent improvements in refinement and active acquisition over the evaluated baselines. Ablations further support the complementary roles of step-wise graph refinement, adaptive risk weighting, and analytically anchored calibration.
