# Jag

Jag replaces coordinate-by-coordinate generation in visual grounding with direct regression of a bounding box in a single forward pass. Autoregressive multimodal models write a box as a coordinate sequence, so sequential decoding adds computation on top of the image-text processing that localization already needs. Jag attaches a lightweight regression head to Qwen3.5-0.8B, reads the last valid input-token state, and predicts all four continuous coordinates together under L1 and GIoU losses that jointly adapt the language backbone, visual merger, and head. On RefCOCO, RefCOCO+, and RefCOCOg it improves accuracy over its base model on every test split, with 65.97 ms mean single-request latency.

Jag replaces coordinate-by-coordinate generation in visual grounding with direct regression of a bounding box in a single forward pass. Autoregressive multimodal models write a box as a coordinate sequence, so sequential decoding adds computation on top of the image-text processing that localization already needs. Jag attaches a lightweight regression head to Qwen3.5-0.8B, reads the last valid input-token state, and predicts all four continuous coordinates together under L1 and GIoU losses that jointly adapt the language backbone, visual merger, and head. On RefCOCO, RefCOCO+, and RefCOCOg it improves accuracy over its base model on every test split, with 65.97 ms mean single-request latency.