提出新数据集，让大模型学会否定动作，提升因果推理能力。
Learning to Deny: Action Denial in Multimodal Large Language Models
- 构建配对视频数据集，对比动作存在与不存在场景。
- 20个主流模型在否定任务上准确率低于50%。
- 引入因果提示可显著减少误判，适合研究视觉推理者。
多模态大语言模型在视频理解方面进展迅速，已在标准基准上实现优异的零样本和少样本识别能力。然而，当存在强烈上下文线索时，模型能否正确判断某项活动并未发生，这一能力仍基本未被探索。本文提出UCF101-AD，一个大规模基准数据集，包含成对的‘动作存在’与‘动作否定’视频片段，用于评估该否认能力。每个否定视频保留了与正样本相同的上下文和运动线索（如人物、物体、场景），但核心动作明确缺失。对20个前沿多模态大模型的评估显示，模型在正类任务上超过85%准确率时，在对应否定任务上准确率下降至50%以下，表明其倾向于确认可能发生的动作而非验证其真实发生。这暴露了现代视频理解模型在因果推理上的关键盲点：无法判断运动是否真正发生。为此，本文提出因果图形式化方法CausalAct，通过自然语言提示将场景结构表达为上下文、交互与运动之间的关联。引入此类因果提示能显著降低误报，证明否定是一种可习得的推理技能。UCF101-AD为诊断和提升多模态模型的因果推理提供了新视角。数据集及代码：https://github.com/raiyaan-abdullah/Learn-to-Deny。
原文摘要 · Abstract (English)
Multimodal large language models (MLLMs) have rapidly advanced video understanding, achieving strong zero-shot and few-shot recognition across standard benchmarks. Yet their ability to deny an action by recognizing when an activity is not happening despite strong contextual cues remains largely unexplored. We introduce UCF101-AD, a large-scale benchmark consisting of paired Action-Presence and Action-Denial clips, designed to evaluate this capacity for denial. Each negative video in UCF101-AD preserves the same contextual and motion cues, including persons, objects, and locations, as its positive counterpart, but the defining action itself is explicitly absent. Evaluating 20 state-of-the-art MLLMs reveals a consistent failure: models that exceed 85% accuracy on the positive action classes collapse below 50% on their action-denial counterparts, indicating a strong inclination to affirm plausible actions rather than verify that they truly occur. This exposes a critical blind spot in modern video understanding: the inability to reason causally about whether a motion actually happens. To probe this issue, we explore a causal graph formulation, CausalAct, which expresses scene structure through natural-language prompts linking context, interaction, and motion. Incorporating such causal cues substantially reduces false positives, demonstrating that denial is a learnable reasoning skill. UCF101-AD provides a new lens for diagnosing and improving causal reasoning in multimodal models. Dataset and relevant code: https://github.com/raiyaan-abdullah/Learn-to-Deny.
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