{
  "id": 12080178,
  "title": "What Should World Models Forget? Stratified Retention for Continual Adaptation",
  "url": "https://urgent.news/2026/10/02/what-should-world-models-forget-stratified-retention-for-continual",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-02T17:58:14.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.03713v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary prediction target, where a correct label remains correct indefinitely. World models do not satisfy this condition. Their prediction target is the environment, which changes, so knowledge that was accurate when acquired may later become false, and discarding…",
  "key_points": [],
  "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."
}