Why decoding boxes one token at a time is slower than it needs to be

Vision-language models typically generate bounding boxes one token at a time, a bottleneck that also breaks the geometric structure of boxes. LocateAnything decodes entire boxes in parallel as atomic units, improving both speed and accuracy. Paired with a 138-million-sample dataset, it achieves higher throughput and better high-IoU localization across multiple benchmarks.