We propose a method to successfully adapt LargeVision-Language-Action (VLA) models, originally pre-trainedon autonomous driving datasets, to the micro-scale domain ofsurgical robotics. We identify the Magnitude Domain Gap—a1000x spatial discrepancy between driving actions (meters) andsurgical actions (millimeters)—as the primary cause of trajectoryparalysis during naive fine-tuning. By introducing DifferentialScale Normalization paired with a novel Variance-IncentivizedRecovery Loss (Lvar ) during motor-cortex adaptation, we demon-strate that the Alpamayo-Surgical system (based on a 10B-parameter driving VLA) can achieve native millimeter precisionin surgical environments while retaining its zero-shot semanticreasoning capabilities. Evaluating on the SutureBot (Tissue 1)dataset, we observe that our technique successfully restorescomplex 3D tool articulation from a previously "frozen" state,achieving a final Mean Average Displacement Error (ADE) of5.53 mm relative to human expert demonstrations. Crucially, theVLA’s Chain-of-Causation reasoning traces correctly verbalizesurgical intent while aligning magnitude predictions natively withthe ground truth.