Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design

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 strategy ranks candidate designs by a product-form marginal valid-hit score, but selecting the highest-ranked candidates independently within a batch can produce redundant recommendations under predictive uncertainty. We introduce ARC-SC (Anchored Risk-Constrained Scenario Coverage), which 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. For fixed anchors and a fixed admissible pool, the scenario-coverage objective is monotone submodular and admits the standard greedy-completion guarantee. In frozen-oracle closed-loop simulations, ARC-SC yields a statistically supported improvement in first-hit discovery, while remaining competitive with directionally favorable performance on higher-dimensional tasks. These results support scenario-aware batch diversification as a principled complement to strong marginal acquisition for early valid-target discovery under experimental failure.