Long-video multimodal question answering requires structured reasoning over visual evidence and dialogue, but Large Vision-Language Models (LVLMs) are constrained by context-window and compute limits. We propose POVQA, which compresses each second into a temporally pooled image (1 fps pooled images) to maintain dense temporal coverage under a fixed token budget. We then train Qwen2.5-VL-7B with supervised fine-tuning (SFT) on rationale+answer targets, and optionally apply Direct Preference Optimization (DPO) for preference alignment. We introduce ReasonVQA as a pilot diagnostic dataset with 12 movies and 239 human-annotated QA+rationale triplets for controlled analysis of long-context multimodal reasoning under compression. On ReasonVQA, SFT improves the best pooled-only baseline from 0.212 to 0.550 F1, showing that pooled evidence plus rationale supervision provides the main performance gains in this setting. In zero-shot transfer, POVQA also reaches 64.7\% on TVQA after SFT+DPO. These results are preliminary: ReasonVQA is small, pooling can lose fine-grained temporal order, and DPO effects are not uniformly positive across settings. Code, dataset, and additional qualitative evaluations are available at \href{https://povqa.github.io}{https://povqa.github.io}.

翻译：长视频多模态问答需要对视觉证据和对话进行结构化推理，但大型视觉语言模型受限于上下文窗口和计算资源限制。我们提出POVQA方法，通过将每秒压缩为时间池化图像（1帧/秒的池化图像），在固定令牌预算下保持密集的时间覆盖。我们采用监督微调在推理+答案目标上训练Qwen2.5-VL-7B，并可选地应用直接偏好优化进行偏好对齐。我们引入ReasonVQA作为试点诊断数据集，包含12部电影及239个人工标注的问答+推理三元组，用于受控分析压缩条件下的长上下文多模态推理。在ReasonVQA上，监督微调将最佳纯池化基线从0.212提升至0.550 F1分数，表明池化证据与推理监督相结合在此场景下取得了主要性能增益。在零样本迁移中，POVQA在应用监督微调+直接偏好优化后于TVQA上达到64.7%准确率。这些结果尚属初步：ReasonVQA规模较小，池化可能丢失细粒度时间顺序，且直接偏好优化的效果在不同场景下并非一致正向。代码、数据集及更多定性评估结果请访问 \href{https://povqa.github.io}{https://povqa.github.io}。