{
  "id": 19039,
  "url": "https://arxiv.org/abs/2608.12078v1",
  "title": "Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models",
  "summary": "Learning world models from offline trajectories enables agents to accomplish different tasks through planning. Object-centric (OC) representations, which decompose a scene into a set of slots that bind to its objects, have been proposed as an inductive bias for world models that are more sample-efficient and generalize better. Yet prior object-centric world models (OCWMs) take the slot encoder as given and evaluate only in-distribution, leaving open whether the object-centric bias actually deliv",
  "authors": "Shukrullo Nazirjonov, Sai Prasanna, Anna Manasyan, Georg Martius",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T14:02:36.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19039",
  "original_url": "https://arxiv.org/abs/2608.12078v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}