Caved or Convinced: Temporal Sampling Gates Claim Deference in Video Large Language Models
When asked which of two events came first, video large language models can fail in two opposite ways: cave to a false claim, or reject a true one. Prior video sycophancy work measures only the first and mitigates it by teaching the model to trust the user less, a fix known in text and image models to worsen the second. In video, both failures come from two causes the literature treats as one: availability, whether the sparse sampled frames contain the two events, and weighting, whether that evidence is trusted over the user. We separate them with two interventions that keep the claim fixed: a frame-preserving reorder that flips the claim's truth, and a sampling-offset shift that captures ...
| Authors | Yuxin Cao, Wei Song, Jingling Xue et al. |
|---|---|
| Primary category | cs.MM |
| Published | 4 August 2026 |
| Last updated at source | 18 August 2026 |
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