让AI看视频推理时间关系，准确率超81%
Temporal Evidence Routing with Structured Visual Evidence for TimeLogicQA
- 分步处理：先提取问题中的事件与时间操作，再按视频时长和难度分配证据
- 用多模态大模型生成结构化视觉证据，程序验证补全动作区间
- 三重验证机制确保答案可信，适合需要精确时间推理的场景
TimeLogicQA评估视频问答系统在事件存在、顺序、持续、边界和重叠等时间关系上的推理能力。本文提出一种视觉证据路由流水线，将感知与符号化时间推理分离。系统首先将问题解析为事件目标、回答模式、候选选项和时间操作符。根据视频时长和操作符难度，分别采用全帧有序证据（短片段）或事件聚焦候选窗口（长视频）。多模态大语言模型生成相关事件的结构化视觉证据，程序化验证器恢复密集动作区间，确定性缩减器应用特定操作符的时间规则生成最终答案。保守融合机制仅在视觉证据、时间程序和置信度检查一致时接受答案，有效减少噪声误判。在官方测试集上，系统平均准确率达81.8。
原文摘要 · Abstract (English)
TimeLogicQA evaluates whether video question answering systems can reason over temporal relations such as event existence, ordering, persistence, boundary conditions, and overlap. We address this task with a visual evidence routing pipeline that separates perception from symbolic temporal reasoning. The system first parses each question into event targets, answer mode, candidate options, and temporal operators. It then routes videos according to duration and operator difficulty, using ordered full-frame evidence for short clips and event-focused candidate windows for long videos. A multimodal large language model produces structured visual evidence for the relevant events, while programmatic verifiers recover dense action intervals and a deterministic reducer applies operator-specific temporal rules to produce the final answer. Conservative fusion accepts an answer only when the visual evidence, temporal program, and confidence checks agree, reducing noisy answer flips. On the official test evaluation, our final system achieves an AvgAcc of 81.8.
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