The paper, posted on arXiv, argues that current Large Multimodal Models (LMMs) are poorly suited to comparing videos for semantic similarity, because they are primarily trained with single-video objectives. To address this, the authors propose framing video similarity estimation as a video-language entailment task, where the relationship between a video and a language statement is used to infer how similar two videos are.
The abstract begins to contrast this approach with prior work, noting that most earlier efforts have focused on computing similarity directly, but the sentence is cut off in the source. As a result, the preprint does not yet provide enough detail to evaluate the method's architecture, training data, or experimental results.
The significance of the work lies in highlighting a clear limitation of current LMMs and suggesting a different formulation for a task that remains challenging. The preprint is available at arXiv:2610.24786.