Where World Models Break: Natural-Input Failure Discovery
World models predict action-conditioned futures and serve as critical internal simulators for downstream planning and control. However, catastrophic prediction failures of world models could dangerously propagate through the control pipeline, as subsequent agent or model training and decision-making depend heavily on the continuous environment evolution forecasted by these world models. Existing evaluations overlook this systemic risk: by aggregating average errors over benign generations from general queries, they fail to stress-test the model against catastrophic collapses under rare or unobserved condition-action combinations. To bridge this gap, we formalize the natural-input failure ...
| Authors | Zhanpeng Shi, Zi Liang, Rong Feng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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