{
  "id": 4529,
  "url": "https://arxiv.org/abs/2605.11259v1",
  "title": "Template-as-Ontology: Configurable Synthetic Data Infrastructure for Cross-Domain Manufacturing AI Validation",
  "summary": "LLarge language model (LLM)-based AI agents deployed in manufacturing environments require populated, schema-correct data for validation, yet production MES data is proprietary, privacy-encumbered, and vendor-specific. This paper introduces the Template-as-Ontology principle: a single Python configuration module (700-770 lines, 45 validated exports) serves simultaneously as the specification for a time-stepped manufacturing simulator and as the runtime domain schema for AI analytics tools, produ",
  "authors": "Grama Chethan",
  "category": "research",
  "topics": "privacy-surveillance,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T21:36:05.000Z",
  "fetched_at": "2026-07-14T16:31:03.580Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4529",
  "original_url": "https://arxiv.org/abs/2605.11259v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}