MoP-JEPA: Hard-Assigned Predictor Mixtures for Stochastic JEPA World Models

• URL: http://arxiv.org/abs/2607.05238v2
• Date: Sat, 11 Jul 2026 09:43:42 GMT
• Title: MoP-JEPA: Hard-Assigned Predictor Mixtures for Stochastic JEPA World Models
• Abstract summary: MoP-JEPA produces a finite candidate set in one pass.<n>On held-out OGBench transitions, graph search with single-output predictors succeeds on $0.02$--$0.09$ of queries, whereas MoP-JEPA reaches $0.85$.
• Score: 14.76812870029721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: JEPA world models commonly predict the next latent state with one regressor. Under stochastic transitions, squared and cosine regression return the conditional mean and its normalized direction, respectively: a single compromise that may match no valid successor. MoP-JEPA instead uses $K$ hard-assigned heads and a context-only router to produce a finite candidate set in one pass. On held-out OGBench transitions, graph search with single-output predictors succeeds on $0.02$--$0.09$ of queries, whereas MoP-JEPA reaches $0.85$. To distinguish useful successors from indiscriminate coverage, we also measure verified-route success (\emph{realroute}), which checks after graph construction whether the proposal contains a path of real transitions. MoP-JEPA leads this same-protocol metric on all three mazes; an MDN attains high raw coverage but predicts many nonexistent edges.

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