{
  "id": 6218,
  "url": "https://arxiv.org/abs/2604.06571v1",
  "title": "LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources",
  "summary": "Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative web profiles. Variations in layout, terminology, and data quality impede rapid triage, large-scale analysis, and search-planning workflows. This paper introduces the Guardian Parser Pack, an AI-driven parsing and normalization pipeline that transforms multi-source investigative documents into a unified, schema-compliant representation suitable for",
  "authors": "Joshua Castillo, Ravi Mukkamala",
  "category": "research",
  "topics": "children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-08T01:35:56.000Z",
  "fetched_at": "2026-07-14T16:32:20.055Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6218",
  "original_url": "https://arxiv.org/abs/2604.06571v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}