One Model, Two Physical Stories: Auditing Misalignment in Multi-Modal World Modeling
World models, systems that generate what happens next given current environmental conditions, are increasingly being implemented with multi-modal generation in mind. However, generating multiple modalities simultaneously, such as visual simulations alongside physical state predictions in the form of text, introduces the risk of cross-modal inconsistency. Tested separately, both outputs may look convincing while still disagreeing: a model can calculate that a ball should rebound in one modality, then generate no rebound in another modality, to say nothing of diverging from real-world dynamics entirely. In this work we focus on two failures explicitly: \emph{Internal misalignment}, the disa...
| Authors | Geigh Zollicoffer, Minh Vu, Rajiv Ranasinghe et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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