Imagine asking an AI to pinpoint a specific moment in a video, like when a basketball player scores a winning shot. Now, imagine asking a slightly different question about the same moment, or showing it the same video but with the game highlights shifted to a different part of the timeline. Would the AI still be able to identify the key moment correctly? Surprisingly, many advanced AI models struggle with this type of temporal consistency, raising crucial questions about their true video comprehension abilities. Researchers are delving deep into this issue, exploring why these video large language models (Video-LLMs) stumble when tested for robustness and trustworthiness in understanding time. The core problem lies in their sensitivity to seemingly minor variations. Change the wording of a question slightly, shift the location of a scene within the video, or ask the AI to verify its own prediction, and suddenly its grasp on the temporal context wavers. This inconsistency is a major roadblock to building truly reliable video AI. Existing solutions like clever prompting or fine-tuning with specific instructions have shown limited success, often improving performance in some areas while causing unexpected declines in others. A new approach called Event Temporal Verification Tuning (VTune) offers a promising path forward. By explicitly training the models to recognize and correct inconsistencies, VTune strengthens their ability to discern whether the visual content truly aligns with the given text query, boosting both accuracy and consistency. While the field is still evolving, this research underscores a critical challenge in video AI: ensuring that our models don’t just react to keywords but genuinely understand the flow and context of time within videos.