{
  "id": 37,
  "url": "https://arxiv.org/abs/2607.10588v1",
  "title": "Constraint-Aware Hierarchical Search for Regulation-Driven Fine-Grained Classification",
  "summary": "Tasks such as customs tariff classification, export control categorization, and standards-based equipment coding require assigning an input instance to a fine-grained class under an explicit regulatory hierarchy. Unlike standard text classification, the correct label in these tasks is not determined by semantic similarity alone, but by rule-defined boundaries, threshold conditions, exclusion clauses, definitions, and local exceptions. As a result, two highly similar inputs may require different ",
  "authors": "Siyu Wang, Wei Tan, Lulu Chen",
  "category": "research",
  "topics": "regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-12T05:57:22.000Z",
  "fetched_at": "2026-07-14T14:14:15.664Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/37",
  "original_url": "https://arxiv.org/abs/2607.10588v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}