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

arXiv:2609.16071v1 Announce Type: cross Abstract: 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 (RMSE=0.811pm0.022) and (0.813pm0.022). 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 (RMSE=0.546), (R^2=0.012), and (NLL=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 (0.495pm0.004) over three seeds, but degrades target calibration ((NLL=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.

Related

• Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
• Flow-JEPA: Flow Matching for Robust Latent Dynamics in JEPA World Models
• ATM: Action-Consistency Transfer Matrix for Diagnosing and Improving Latent World Models

Source: arXiv cs.AI | 2026-09-16