Schema-Adaptive Action-Conditioned JEPA for Cross-Machine CNC Transfer under Partial Sensor Overlap

Cross-machine deployment of industrial world models requires transfer across changes in dynamics, sensing interfaces, sampling regimes, and control units. We study a schema-adaptive action-conditioned Joint-Embedding Predictive Architecture (SAAC-JEPA) for CNC dynamics, where the source machine has 17 canonical sensor channels and the target shares only 10. Evaluation uses group-disjoint source splits, source-only normalization, held-out self-supervised validation, unit audits, and a sealed target test after model locking. Across five seeds, JEPA pretraining gives no clean-source forecasting gain: scratch and pretrained-body models obtain $\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=0.811\pm 0.022$ and $0.813\pm 0.022$. A source-only search over 20 candidates selects a schema-consistent action-conditioned JEPA after seven-seed stability checks. On the confirmatory target pass, the locked model reaches zero-shot $\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=0.546$, $R^2=0.012$, and $\mathrm{N}\mathrm{L}\mathrm{L}=0.52$, outperforming persistence but not RevIN-equipped PatchTST and iTransformer baselines ($0.503$ and $0.498$). A pre-declared paired ablation shows that RevIN in the same architecture improves RMSE to $0.495\pm 0.004$ over three seeds, but degrades target calibration ($\mathrm{N}\mathrm{L}\mathrm{L}=20.6$) on stationary context windows. A pre-lock adaptation sweep further reduces RMSE to $0.520$ with limited target support. These results show that source-domain forecasting accuracy alone is insufficient to assess industrial predictive representations, and that cross-machine adaptation under partial sensor overlap is a distinct evaluation axis.