Learning Counterfactual World Models for Embodied Reasoning under Partial Observability
World models promise a general route to embodied intelligence: learn predictive dynamics once, then reason, plan, and act with them. Increasingly, the representations beneath such models are pretrained on large-scale video, interaction, and multimodal corpora, which raises a question prediction quality alone cannot answer: when is a learned representation actually actionable? We identify a failure mode we call counterfactual collapse: a model predicts visually plausible futures while failing to distinguish interventions with different behavioral consequences. This arises whenever a representation is optimized for perceptual similarity rather than intervention structure, which is precisely...
| Authors | Todd Y. Zhou, Daniel Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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