# D-JEPA

D-JEPA introduces a decision-aligned latent world model: it first quantifies a decision-local prediction gap in which, among the few futures competing for execution, a candidate predicted closer to the goal can fail while an available alternative succeeds; it then uses executed outcomes to supervise a bounded, permutation-equivariant set operator that learns decision-relevant relations among candidate futures, and realizes the learned decision structure in JEPA-compatible future representations so aligned actions can be read out through native latent-distance planning; it reaches 87.89% success on PushT, a 15.04-point average gain on RoboTwin, a 17-point gain on physical robot tasks, and raises mean driving PDMS from 57.36 to 95.34.

D-JEPA introduces a decision-aligned latent world model: it first quantifies a decision-local prediction gap in which, among the few futures competing for execution, a candidate predicted closer to the goal can fail while an available alternative succeeds; it then uses executed outcomes to supervise a bounded, permutation-equivariant set operator that learns decision-relevant relations among candidate futures, and realizes the learned decision structure in JEPA-compatible future representations so aligned actions can be read out through native latent-distance planning; it reaches 87.89% success on PushT, a 15.04-point average gain on RoboTwin, a 17-point gain on physical robot tasks, and raises mean driving PDMS from 57.36 to 95.34.