{
  "abstract": "An agentic scientific workflow can run to completion and still release wrong results: a silent numerical error, a stale input, or a defect in a shared upstream step can contaminate several outputs while a sibling branch stays correct. We treat trust in such results as a release decision. A harness around the workflow's directed acyclic graph (DAG) decides which outputs may be used: it enforces mandatory checks, contains failures to the affected branch, recomputes from tracked versions, adds validators, and releases a result only if a risk model estimates its probability of being invalid as low. Language-model agents work inside the harness, choosing corrective actions and reviewing releases, but cannot override it. On 60 held-out paired instances of two workflows with injected faults, we switched components on and off. Each supplied a different property. Containment and recovery raised the number of correct results. Validation supplied safety: fixed rules without validation released 37 invalid results, while validating every candidate released none. Risk estimation supplied efficiency, matching that safety with 31% fewer validators at somewhat lower yield, although better targeting than random validation was not shown. Lineage evidence helped on one workflow. The language model added no measurable benefit: whether invalid results escaped depended on the harness, not the model. This proof of concept points to a practical path: placing agents inside explicit release-control structure can keep released scientific results trustworthy, and it gives more capable agents a measurable baseline to improve on.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.19507",
  "author_total": 3,
  "authors": [
    {
      "name": "Ashish Acharya"
    },
    {
      "name": "Aman Al Amin"
    },
    {
      "name": "Hong Qin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/40cnpc530wyq1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.19507",
  "fulltext_url": null,
  "id": "40cnpc530wyq1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.19507",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1802|Scientific Computing and Data Management"
    ]
  ],
  "openalex": "W7222271900",
  "pdf_url": "https://arxiv.org/pdf/2610.19507",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/40cnpc530wyq1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:36Z",
      "native_id": "2610.19507",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.19507"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Structure Before Intelligence: Gating What Agentic Scientific Workflows Release",
  "title_text": "Structure Before Intelligence: Gating What Agentic Scientific Workflows Release",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:36Z",
  "url": "https://research.draconitic.com/paper/40cnpc530wyq1",
  "venue": "arXiv",
  "versions": []
}