D-JEPA: A Decision-Aligned Latent World Model

Hugging Face Daily Papers Papers
latent-world-model decision-alignment ai-research robotics control-systems jepa-model

Summary

D-JEPA is a decision-aligned latent world model that learns decision-relevant relations among candidate futures to improve action selection in tasks like manipulation and autonomous driving, demonstrating significant performance gains.

Latent world models predict the consequences of actions, but accurate prediction does not guarantee that latent distance reflects which candidate will execute successfully. We identify a decision-local prediction gap: among the few futures competing for execution, a candidate predicted closer to the goal can produce a worse realized outcome than an available alternative. We introduce D-JEPA, a decision-aligned latent world model that learns decision-relevant relations among candidate futures from executed outcomes. A bounded, permutation-equivariant operator jointly reasons over goal-relative predictive features and ordinal evidence, refining pretrained predictive geometry where action choices are most consequential. Restricted predictor adaptation and a shared ordinal interface extend this alignment across complementary predictive geometries. D-JEPA further realizes the learned decision structure in JEPA-compatible future representations, enabling deployment through native latent-distance planning. Evaluations across latent control, manipulation, pretrained action-producing models, physical robots and autonomous driving demonstrate improved action selection, including 87.89% success on PushT, a 15.04-point average gain on RoboTwin, and a 17-point gain on physical robot tasks. These results establish decision-relevant relational structure as a direct bridge between predictive world modeling and effective control.
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Cached at: 09/29/26, 12:07 AM

Paper page - D-JEPA: A Decision-Aligned Latent World Model

Source: https://huggingface.co/papers/2609.24749

Abstract

Latentworldmodelspredicttheconsequencesofactions,butaccuratepredictiondoesnotguaranteethatlatentdistancereflectswhichcandidatewillexecutesuccessfully.Weidentifyadecision-localpredictiongap:amongthefewfuturescompetingforexecution,acandidatepredictedclosertothegoalcanproduceaworserealizedoutcomethananavailablealternative.WeintroduceD-JEPA,adecision-alignedlatentworldmodelthatlearnsdecision-relevantrelationsamongcandidatefuturesfromexecutedoutcomes.Abounded,permutation-equivariantoperatorjointlyreasonsovergoal-relativepredictivefeaturesandordinalevidence,refiningpretrainedpredictivegeometrywhereactionchoicesaremostconsequential.Restrictedpredictoradaptationandasharedordinalinterfaceextendthisalignmentacrosscomplementarypredictivegeometries.D-JEPAfurtherrealizesthelearneddecisionstructureinJEPA-compatiblefuturerepresentations,enablingdeploymentthroughnativelatent-distanceplanning.Evaluationsacrosslatentcontrol,manipulation,pretrainedaction-producingmodels,physicalrobotsandautonomousdrivingdemonstrateimprovedactionselection,including87.89%successonPushT,a15.04-pointaveragegainonRoboTwin,anda17-pointgainonphysicalrobottasks.Theseresultsestablishdecision-relevantrelationalstructureasadirectbridgebetweenpredictiveworldmodelingandeffectivecontrol.

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