{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.27873",
  "author_total": 4,
  "authors": [
    {
      "name": "Chuhan Yang"
    },
    {
      "name": "Chenxi Wang"
    },
    {
      "name": "Linhan Wu"
    },
    {
      "name": "Yuyang Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1ksvbnn08r4a0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.27873",
  "fulltext_url": null,
  "id": "1ksvbnn08r4a0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.27873",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7204858532",
  "pdf_url": "https://arxiv.org/pdf/2608.27873",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1ksvbnn08r4a0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:28Z",
      "native_id": "2608.27873",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.27873"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design",
  "title_text": "Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:28Z",
  "url": "https://research.draconitic.com/paper/1ksvbnn08r4a0",
  "venue": "arXiv",
  "versions": []
}