LeCompass: Task-Level Execution through State-Indexed Interfaces

Action-conditioned joint-embedding predictive architectures (JEPAs) offer a foundation for predictive decision-making by modeling the consequences of actions in representation space. Yet evaluating local reachability in latent space does not by itself provide a task-level execution interface: intermediate targets must be generated, local inference has finite budgets, and execution requires spatial feedback and persistent interaction state. We introduce LeCompass, a state-indexed execution framework built around a frozen LeWorldModel (LeWM). A proposer supplies intermediate latent targets, while CompassFlow searches the source space of a learned trajectory prior to obtain effective action candidates under limited predictive-evaluation budgets. Patch-token readouts provide spatial feedback, while observation-based memory supports phase transitions and bounded recovery. State-indexed targets support cross-episode context–goal composition. On 1,000 official TEST full-episode Cube trials, the complete LeCompass execution system achieves 86.9% native 4-cm reach. Separate matched analyses show that predictive ranking raises success from 4.7% to 44.9% on TEST PushT and from 33.8% to 77.7% on TEST TwoRoom, whereas Cube retains 86.4% native reach without predictive ranking. These results demonstrate task-level execution with task-dependent roles for latent target proposal, predictive search, and observation-grounded control.