A boundary faithful backbone still has to survive frame two

The challenge of boundary-faithful backbones in computer vision extends beyond single-frame demos to multi-frame scenarios. While static boundary visualizations and training-free video object segmentation, as seen in the LingBot Vision release, are useful, they don't guarantee temporal boundary faithfulness. A crucial test involves tracking a sharp edge across mildly occluded frames and comparing the boundary token with the mask; if the token drifts, it indicates that single-image results might be performing better than video results in maintaining boundary integrity over time.