Scientifically, JIT-LoRA sits at the intersection of parameter-efficient fine-tuning, online learning, and continual adaptation. Its strongest case is architectural economy: low-rank matrices restrict the update subspace, potentially lowering compute and reducing interference with pretrained representations. The reported zero catastrophic forgetting result is encouraging, but it is not yet a broad safety guarantee. A serious comparison would require fixed baselines, held-out tasks, repeated seeds, forgetting curves, and tests for update-induced fabrication.