Repair or Relocation? When Fixing a Composition Breaks Its Parts

Correcting a model's prediction for a combined change can break its previously correct predictions for the constituent changes. Endpoint accuracy alone cannot distinguish these outcomes. We evaluate repair on controlled geometric quartets by tracking constituent preservation on fixed baseline failures, alongside joint successes gained and lost across the full population. On a fresh bank of 2,348 quartets from 873 parent scenes, 76.6–83.4% of raw-coordinate endpoint corrections introduced constituent errors across three seeds. Joint correctness nevertheless improved by 2.9–6.0 percentage points, showing that incomplete repair can coexist with positive net improvement. Geometric inputs increased constituent-preserving repair by 27.4–33.8 points relative to raw inputs. Focal Distillation reduced regression but also suppressed useful corrections under the tested recipe. Rendered-image experiments showed positive joint gains whose magnitude depended on the training recipe. Repair should therefore be assessed through both constituent preservation and the net change in joint correctness.