{
  "id": 1997,
  "url": "https://link.springer.com/article/10.1007/s10506-026-09525-6",
  "title": "LLM-assisted formalization for deterministic detection of statutory inconsistency in tax law",
  "summary": "This study introduces a hybrid neuro-symbolic framework that achieves deterministic detection of statutory inconsistency in complex law. We use the U.S. Internal Revenue Code (IRC) for our investigation because its complexity makes it a fertile domain for identifying conflicts. Our research advances a solution for detecting inconsistent provisions by combining Large Language Models (LLMs) with a symbolic reasoner. To evaluate this approach, we conducted experiments using GPT-4o, GPT-5, and Prolo",
  "authors": null,
  "category": "research",
  "topics": "regulation,finance-investment",
  "orgs": "openai",
  "regions": null,
  "published_at": "2026-07-10T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-artificial-intelligence-and-law",
  "source_name": "Artificial Intelligence and Law",
  "source_homepage": "https://link.springer.com/journal/10506",
  "ethics_ai_record_url": "https://ethics.ai/record/1997",
  "original_url": "https://link.springer.com/article/10.1007/s10506-026-09525-6",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}