Scope of modeling future uncertainty in video understanding

Determine how far video understanding models should extend beyond deterministic prediction to represent and reason about future uncertainty, including modeling multiple plausible futures and integrating uncertainty into planning and decision-making processes.

Background

The paper argues that real-world environments are fundamentally uncertain and that video understanding should support decision-making under uncertainty, not just deterministic prediction.

This motivates extending models to reason over multiple plausible futures, evaluate goals and costs, and operate effectively in embodied, online settings.

References

Beyond deterministic objectives such as video labeling, an open question is how far video understanding should extend toward understanding future uncertainty.

— Video Understanding: From Geometry and Semantics to Unified Models  (2603.17840 - An et al., 18 Mar 2026) in Third outlook point, Section 6 (Conclusion and Outlook)

Despite its high prediction fidelity, \algname is still prone to hallucinations, like other video world models. Mitigating these hallucinations is critical for trustworthy integration in diverse robotics applications, such as planning, policy evaluation, and policy finetuning, presenting an exciting direction for future work on hallucination detection and mitigation, e.g., via uncertainty quantification~\citep{mei2026worldmodelsknowdont}.

— CLAP: Cross-Embodiment Video World Models are Zero-Shot Physical Simulators  (2608.27406 - Liu et al., 27 Aug 2026) in Section 5, Conclusion, Limitations, and Future Work

The existing version also exhibits limitations. First, the hierarchical slow--fast architecture introduces additional computational and memory overhead, motivating future research on model compression and asynchronous inference. Second, the current model does not explicitly capture multimodal futures or predictive uncertainty, which may limit its performance in ambiguous and rare driving scenarios. We leave solving them as future works.

— Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving  (2609.03572 - Fan et al., 3 Sep 2026) in Conclusion