# Language-Grounded Spatial Verifier

In aerial drone imagery, a referred target often covers only tens of pixels and sits among visually similar vehicles or people, so a generative editor can draw a plausible mask around the wrong instance. DroneSegEdit freezes the open-weight LongCat-Image-Edit, trains only a lightweight LoRA to render candidate regions, then uses a language-grounded spatial verifier to strip foreground inside that candidate that the referring expression does not support. On the complete official RIS-LAD test set it reaches 52.78 mIoU, 7.16 points above the previous best ReSaP-8B, and stays competitive overall on UAVid-RIS and VDD-RIS, with the clearest gains on compact vehicles and humans.

In aerial drone imagery, a referred target often covers only tens of pixels and sits among visually similar vehicles or people, so a generative editor can draw a plausible mask around the wrong instance. DroneSegEdit freezes the open-weight LongCat-Image-Edit, trains only a lightweight LoRA to render candidate regions, then uses a language-grounded spatial verifier to strip foreground inside that candidate that the referring expression does not support. On the complete official RIS-LAD test set it reaches 52.78 mIoU, 7.16 points above the previous best ReSaP-8B, and stays competitive overall on UAVid-RIS and VDD-RIS, with the clearest gains on compact vehicles and humans.