💡 The paper introduces INTACT, a novel approach to learning world models that can predict how actions change a scene and recover actions for a desired change without expensive test-time search. The existing forward latent world models predict how actions change a scene, but recovering actions for a desired change is only possible through expensive test-time search. INTACT turns action-labeled, reward-free trajectories into a deployable intent-to-action interface. Each transition supplies physical intent, while a future goal supplies deployment intent. The architecture is isomorphic between local and goal motion-intent backbone-input graphs through an identical four-slot grammar and shared parameters.

The method provides intact transfer from RGB evidence to action-effective latent intent coordinates and from intent families to their corresponding action-law families. Asymmetric endpoint gradients ground physical successors and fix future goals as anchors, joining representation learning and control without pointwise latent matching or globally linear dynamics. The resulting coordinates support a robust distributional action law, where its conditional means serve directly as a search-free policy, while sampling remains available for diversity or optional verification.

The results show that one-epoch, zero-search models reach high success rates on four official tasks, achieving 85.78, 100.00, 97.67, and 97.89 percent success. Optional local cross-entropy method centered on the Direct plan reaches 96.86 percent macro success using 384 instead of 9000 candidate sequences, reducing sampling by 23.44 times while improving pure cross-entropy method by 16.00 points. One shared four-task encoder reaches 89.39 percent E5 Direct macro and improves every task over jointly trained models, while predicted expert action-family kNN tracks Direct success at 0.954. Direct inference takes 2.9-5.5 milliseconds. Overall, INTACT provides a robust and efficient approach to learning world models that can predict and recover actions for desired changes without expensive test-time search.