{
  "id": 7468,
  "url": "https://arxiv.org/abs/2603.08933v1",
  "title": "Interpretable Markov-Based Spatiotemporal Risk Surfaces for Missing-Child Search Planning with Reinforcement Learning and LLM-Based Quality Assurance",
  "summary": "The first 72 hours of a missing-child investigation are critical for successful recovery. However, law enforcement agencies often face fragmented, unstructured data and a lack of dynamic, geospatial predictive tools. Our system, Guardian, provides an end-to-end decision-support system for missing-child investigation and early search planning. It converts heterogeneous, unstructured case documents into a schema-aligned spatiotemporal representation, enriches cases with geocoding and transportatio",
  "authors": "Joshua Castillo, Ravi Mukkamala",
  "category": "research",
  "topics": "regulation,children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-09T21:08:29.000Z",
  "fetched_at": "2026-07-14T16:33:16.667Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7468",
  "original_url": "https://arxiv.org/abs/2603.08933v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}