When Vision Speaks for Sound

Xiaofei Wen, Wenjie Jacky Mo, Xingyu Fu, Rui(Yanson) Cai, Tinghui Zhu, Wendi Li, Yanan Xie, Muhao Chen, Peng Qi

When Vision Speaks for Sound: 151 upvotes on Hugging Face Daily Papers, #3 of 54 papers on 2026-05-20. Day-by-day upvote history. It lost 11 votes when the Hub removed votes in bulk.

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.

Paper page on Hugging Face · arXiv

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