{"slug": "mind-the-temporal-gap-evaluating-video-models-for-temporal-understanding-in", "title": "Mind the Temporal Gap: Evaluating Video Models for Temporal Understanding in Driving", "summary": "A new arXiv paper (2610.12575v1) finds that modern video-language models remain unreliable temporal reasoners in safety-critical driving settings, with standard prompting, visual prompting, few-shot prompting, and chain-of-thought prompting all proving task- and model-dependent and showing persistent class biases. The authors report that lightweight LoRA finetuning and simple probes trained on intermediate visual features recover strong motion-discriminative performance, indicating motion-relevant cues exist inside the models but are not reliably used by the final video-to-language pathway. The paper concludes that current video-language models often behave more like frame-level, bias-driven recognizers than dependable temporal world models for driving.", "body_md": "arXiv:2610.12575v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/mind-the-temporal-gap-evaluating-video-models-for-temporal-understanding-in", "canonical_source": "https://arxiv.org/abs/2610.12575", "published_at": "2026-10-12 04:00:00+00:00", "updated_at": "2026-10-12 04:42:31.927748+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "artificial-intelligence", "ai-research", "autonomous-vehicles"], "entities": ["arXiv", "LoRA"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/mind-the-temporal-gap-evaluating-video-models-for-temporal-understanding-in", "markdown": "https://wpnews.pro/news/mind-the-temporal-gap-evaluating-video-models-for-temporal-understanding-in.md", "text": "https://wpnews.pro/news/mind-the-temporal-gap-evaluating-video-models-for-temporal-understanding-in.txt", "jsonld": "https://wpnews.pro/news/mind-the-temporal-gap-evaluating-video-models-for-temporal-understanding-in.jsonld"}}