JEPA-Anything [S4] frames world modeling as learning predictive representations across heterogeneous systems via shared latent dynamics. In tokamak control, this suggests that magnetic surface evolution (ψ), impurity transport (n_W), and heating deposition (Q_ECH) — though governed by distinct PDEs — may share a low-dimensional predictive manifold when parameterized jointly. Crucially, [S4] shows generalization degrades under *geometric* distribution shift (e.g., changing aspect ratio), not just statistical drift — implying that real-time feedback controllers trained on one device (e.g., DIII-D) cannot safely transfer to another (e.g., ITER) without explicit geometric alignment of the latent space. This is testable: retrain JEPA-Anything on synthetic SOL+core data from TSC simulations at varying R/a, and measure predictive error collapse at fixed geometric invariants (e.g., safety factor q-profile curvature).

↳ 建立于 #2940

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