Despite rapid progress in video-capable MLLMs, we find that their apparent audio understanding in videos is often vision-driven: models rely on visual cues to infer or hallucinate acoustic information, rather than verifying the audio stream. This issue appears across both state-of-the-art open-source omni models and leading closed-source models from providers such as Google and OpenAI. We characterize this failure mode as an audio-visual Clever Hans effect, in which models appear (falsely) audio-grounded, but actually exploit visual-acoustic correlations without verifying whether the audio and visual streams are truly aligned. To systematically study this behavior, we introduce Thud, an intervention-driven probing framework based on three counterfactual audio edits: Shift, which tests temporal synchronization; Mute, which tests sound existence; and Swap, which tests audio-visual consistency. Beyond diagnosis, we further study a two-stage alignment recipe: intervention-derived preference pairs teach audio verification, while event-level general video preferences regularize the model against over-specialization. Our best 10K-sample recipe improves average performance across the three intervention dimensions by 28 percentage points, while slightly improving performance on general video and audio-visual QA benchmarks.

翻译：尽管视频多模态大语言模型（MLLMs）取得了快速进展，但我们发现其对视频中音频内容的理解往往"视觉驱动"：模型依赖视觉线索推断甚至臆想声学信息，而非验证真实音频流。这一问题同时存在于当前最先进的开源全能模型及谷歌、OpenAI等厂商的闭源模型中。我们将此类故障模式定义为"音视频克利弗·汉斯效应"——模型看似具备音频理解能力，实则利用视觉-声学相关性进行判断，却未验证音视频流是否真正对齐。为系统性研究该行为，我们提出Thud框架，这是一种基于三种反事实音频编辑的干预驱动探测方法：Shift（测试时间同步性）、Mute（测试声音存在性）及Swap（测试音视频一致性）。除诊断功能外，我们进一步研究了两阶段对齐策略：干预衍生偏好对用于训练音频验证能力，而事件级通用视频偏好则防止模型过度专业化。采用最优的10K样本方案后，模型在三种干预维度上的平均性能提升28个百分点，同时通用视频及音视频问答基准测试性能略有提升。