Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models
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
Record details
Published: 12 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Bias & fairness · Agents & autonomy
Retrieved: 13 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
arXiv · 10 August 2026
Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent
arXiv cs.AI · 4 August 2026
Implementing Causal Perception: Competing SCMs and Situated Fairness
arXiv · 4 August 2026
Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent
HuggingFace Daily Papers · 3 August 2026
Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints
arXiv fairness query · 3 August 2026
Douyin Multimodal Embedding Model Technical Report
HuggingFace Daily Papers · 2 August 2026
How to cite this record
ethics.ai (12 August 2026), “Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models,” evidence record 19039, https://ethics.ai/record/19039 (originally published by arXiv cs.AI).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.