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Even state-of-the-art Video-LLMs fail to generalize to dynamic aerial environments once evaluation moves beyond repetitive question templates and static satellite imagery.
Negation, a known weakness in VLMs like CLIP, can be dramatically improved by strategically fine-tuning only the *front* layers of the text encoder with a modified contrastive loss.