# Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models

arXiv: https://arxiv.org/abs/2608.12078 · HTML: https://mlsanity.com/paper/2608.12078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Latent predictive world models, one of 11 directions in World models.
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 12 Aug 2026.

## Placed in

- World models / [Latent predictive world models](https://mlsanity.com/wm/latent-predictive-world-models/)

## Builds on (most cited first, 8 of 8)

- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [MONet: Unsupervised Scene Decomposition and Representation](https://mlsanity.com/paper/1901.11390/) (2019)
- [LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels](https://mlsanity.com/paper/2603.19312/) (2026)
- [Causal-JEPA: Learning World Models through Object-Level Latent Masking](https://mlsanity.com/paper/2602.11389/) (2026)
- [Learning Interactive World Model for Object-Centric Reinforcement Learning](https://mlsanity.com/paper/2511.02225/) (2025)
- [Object-Centric World Models from Few-Shot Annotations for Sample-Efficient Reinforcement Learning](https://mlsanity.com/paper/2501.16443/) (2025)
- [stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation](https://mlsanity.com/paper/2602.08968/) (2026)
- [Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations](https://mlsanity.com/paper/2605.14937/) (2026)

## Abstract (arXiv)

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 delivers for planning and what within the OCWM drives it. We conduct a controlled study of OCWMs for visual model-predictive control along two axes: object-centric representation quality and generalization under distribution shift relative to scene-centric models. We find that (i) planning success correlates positively with unsupervised slot-quality metrics (FG-ARI, mBO), though the gains saturate at high slot quality; (ii) with well-bound slots, the auxiliary proprioception inputs and masking inductive bias that prior methods relied on become unnecessary; and (iii) under unseen distribution shifts, the OCWM with well-bound slots is more robust overall than the end-to-end trained scene-centric LeWM, while DINO-WM, built on similar frozen pretrained features, remains competitive -- pointing to pretrained features as a key contributor to robustness.