AI看视频只会认物体，不会推理逻辑
现在的AI看视频，能认出画面里有一只猫、一个球，但如果你问它“猫先碰了球，还是球先滚到墙角？”它就懵了。这篇论文专门设计了一套测试，把视频推理拆成5种基本逻辑操作：跟踪状态、顺序计数、时间排序、动态空间、结构组合。结果发现，最强AI模型在这些任务上和人差距巨大，复杂度一高就崩。它不是你明天能用上的，但揭示了AI的深层短板：能看，但不会想。
📄 原文摘要(英文)
Recent interest in multimodal large language models (MLLMs) raises a central question: can they reason over dynamic visual evidence rather than merely recognize objects or events in individual frames? This ability, which we refer to as video temporal-logical reasoning, requires models to maintain, update, and compose evidence as visual states evolve across frames. Existing video benchmarks often conflate this capability with scene complexity, static recognition, or uncontrolled temporal variation. To isolate this capability, we introduce Video-MME-Logical, a controlled benchmark organized around five temporal-logical operations: state tracking, sequential counting, temporal ordering, dynamic spatiality, and structural composition. The benchmark contains 25 fine-grained task categories generated with controlled object states, transitions, temporal dependencies, and logical compositions. It enables difficulty-controlled final-answer evaluation by varying temporal horizon and reasoning complexity, and supports intermediate-state diagnostics by verifying whether models recover the required logical reasoning trace before producing the final answer. Experiments with state-of-the-art MLLMs reveal a substantial human-model gap, especially as temporal-logical complexity increases. Supervised fine-tuning on up to 500K generated samples improves performance but remains insufficient to close the reasoning gap, positioning Video-MME-Logical as a scalable testbed for analyzing and improving temporal-logical reasoning in MLLMs.