AI 中文总结
针对易故障闭环逆设计的首命中发现问题，提出ARC-SC方法，在超导和JARVIS基准上实现首命中发现的统计显著改进，为结构化实验故障下的早期有效目标发现提供了新的批量策略。
AI 中文摘要
在易故障的闭环逆设计中，尽早发现至少一个满足目标要求的有效设计是核心目标。一种自然的批量基线方法通过乘积形式的边际有效命中得分对候选进行排序，但在预测不确定性下独立选择排名最高的候选会产生冗余推荐并浪费实验预算。我们提出ARC-SC（Anchored Risk-Constrained Scenario Coverage，锚定风险约束场景覆盖），这是一种批量采集方法，它将优质边际候选保留为锚点，并在风险支持约束下，通过最大化预测目标场景的互补覆盖来分配剩余的批量位置。在超导和JARVIS材料属性基准的冻结预言机闭环模拟中，ARC-SC在首命中发现方面取得了统计显著的改进，且在更具挑战性的设计空间中，其首命中性能仍与方向有利的方法具有竞争力。这些结果确立了ARC-SC作为一种以POF为锚点、感知场景的批量策略，用于在结构化实验故障下改进早期有效目标的发现。
英文摘要
Early discovery of at least one valid design satisfying a target requirement is a central objective in failure-prone closed-loop inverse design. A natural batch baseline ranks candidates by a product-form marginal valid-hit score, but selecting the highest-ranked candidates independently can produce redundant recommendations under predictive uncertainty and waste the experiment budget. We introduce ARC-SC(Anchored Risk-Constrained Scenario Coverage), a batch acquisition method that preserves strong marginal candidates as anchors and allocates the remaining batch positions by maximizing complementary coverage over predictive target scenarios under a risk-support constraint. In frozen-oracle closed-loop simulations on superconductivity and JARVIS materials-property benchmarks, ARC-SC yields a statistically supported improvement in first-hit discovery and remains competitive with directionally favorable first-hit performance on more challenging design space. These results establish ARC-SC as a POF-anchored, scenario-aware batch strategy for improving early valid-target discovery under structured experimental failure.
