{
  "id": 19569766,
  "title": "Semantic diffing of formal policies with anti-unification",
  "url": "https://urgent.news/2026/11/11/semantic-diffing-of-formal-policies-with-anti-unification",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-11T23:33:03.000Z",
  "source": {
    "name": "AWS Machine Learning",
    "slug": "aws-machine-learning",
    "url": "https://aws.amazon.com/blogs/machine-learning/semantic-diffing-of-formal-policies-with-anti-unification/"
  },
  "original_language": "en",
  "account": "When examining formal policies, a textual diff may highlight syntactic changes that don't impact meaning. These changes, such as reordering conjuncts, swapping arguments of equality, or using equivalent predicates, can make a file appear altered, while a real change in behavior, like an \"or\" where \"and\" is required, could be overlooked. A semantic diff reveals both commonalities and differences between two policies.",
  "summary": "A textual diff of two formal policies flags every cosmetic change and hides the one that alters behavior. This post shows how to compute a symbolic, deterministic semantic diff with an open-source anti-unification engine, so reviewers see only the genuine disagreements between two candidate authorization policies.",
  "key_points": [
    "Semantic diffing highlights syntactic changes without impact on meaning.",
    "Commonalities and differences between two policies are revealed.",
    "Anti-unification addresses issues like reordering conjuncts or swapping arguments."
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}