Mind the Temporal Gap: Evaluating Video Models for Temporal Understanding in Driving

Abstract:Modern video-language models are increasingly treated as general-purpose video reasoners, yet it remains unclear whether they robustly capture temporal dynamics in safety-critical settings such as driving. We study this question through controlled ego-motion tasks that require identifying actions from driving videos. Across video-language models, we find that standard prompting, visual prompting, few-shot prompting, and chain-of-thought prompting remain unreliable and highly task- and model-dependent, with persistent class biases, although newer models show substantial task-specific few-shot gains. In contrast, lightweight LoRA finetuning and simple probes trained on intermediate visual features recover strong motion-discriminative performance, indicating that motion-relevant cues are often present inside the models but are not reliably used by the final video-to-language pathway. These results suggest that, in driving, current video-language models remain unreliable temporal reasoners and often behave more like frame-level, bias-driven recognizers than dependable temporal world models.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2610.12575 [cs.CV]

(or arXiv:2610.12575v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.12575 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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