Despite Video Large Language Models~(Video-LLMs) have rapidly advanced in recent years, the perception hallucination issue has emerged as a significant bottleneck, hindering their real-world applicability. While several methods for hallucination mitigation have been proposed, they often compromise the model’s capacity for video understanding and reasoning. In this work, we propose SmartSight, a pioneering step to address this issue in a training-free manner by leveraging the model’s own introspective capabilities. Specifically, SmartSight generates multiple candidate responses to uncover low-hallucinated outputs that are often obscured by standard greedy decoding. It assesses the hallucination of each response using the Temporal Attention Collapse score, which measures whether the model over-focuses on trivial temporal regions of the input video when generating the response. To improve efficiency, SmartSight identifies the Visual Attention Vanishing point, enabling more accurate hallucination estimation and early termination of hallucinated responses, reducing decoding cost by up to 79.6%. Experiments show that SmartSight substantially lowers hallucinations for QwenVL-2.5-7B by 10.59% on VRIPT-HAL, while simultaneously enhancing video understanding and reasoning, boosting performance on VideoMMMU by 8.86% and surpassing the proprietary model Gemini 1.5 Pro. Consistent improvements are observed across 10 diverse Video-LLMs. These results highlight SmartSight’s effectiveness as a general solution for improving the reliability of state-of-the-art open-source Video-LLMs.