AI 中文总结
研究针对3D点云分类器，提出MiSS框架，通过扰动相对充分性推理来解释。该框架将候选提议与验证分开，由加权MaxSAT过程提议联盟，黑盒统计预言机确定充分性，实验表明其在精度、覆盖率和解释时间上优于基于规则的基线和穷举搜索。
AI 中文摘要
我们提出了MiSS，这是一个基于黑盒、查询的框架，用于通过扰动相对充分性推理来解释3D点云分类器。MiSS将超点分区视为可解释的抽象层，并询问在指定扰动分布下，能否从几何区域的最小联盟中验证原始预测。与需要布尔特征空间或预测器的白盒逻辑编码的归纳解释器不同，MiSS将候选提议与验证分开：加权MaxSAT过程使用启发式自适应基数下限、经认证的精确大小回退、安全收紧的上限、阻塞子句以及从先前预言机评估中学到的代理获取启发式来提议联盟，而黑盒统计预言机则根据预测查询来确定充分性。该系统返回经过统计验证的充分联盟作为二元归因，在认证搜索完成时保证最小基数。在ModelNet40和ShapeNet上使用PointNet和PointMLP分类器进行的实验表明，在大多数设置下，与基于规则的基线相比，具有更高的精度和覆盖率，且解释时间比穷举搜索更短。
英文摘要
We present MiSS, a black-box, query-based framework for explaining 3D point cloud classifiers through perturbation-relative sufficiency reasoning. MiSS treats a superpoint partition as an interpretable abstraction layer and asks whether the original prediction can be certified from a minimal coalition of geometric regions under a specified perturbation distribution. Unlike abductive explainers that require Boolean feature spaces or white-box logical encodings of the predictor, MiSS separates candidate proposal from verification: a weighted MaxSAT procedure proposes coalitions using a heuristic adaptive cardinality floor, certified exact-size fallback, a safely tightened upper bound, blocking clauses, and a surrogate acquisition heuristic learned from previous oracle evaluations, while a blackbox statistical oracle decides sufficiency from prediction queries. The system returns a statistically verified sufficient coalition as a binary attribution, with minimum cardinality guaranteed when certified search completes. Experiments on ModelNet40 and ShapeNet with PointNet and PointMLP classifiers show higher precision and coverage than rule-based baselines in most settings, with lower explanation time than exhaustive search.
