Making Latent Evolution Explicit: Operator-Structured Transitions for World Action Models

• URL: http://arxiv.org/abs/2608.27259v1
• Date: Thu, 27 Aug 2026 15:43:44 GMT
• Title: Making Latent Evolution Explicit: Operator-Structured Transitions for World Action Models
• Abstract summary: World Action Models (WAMs) augment robot policies by predicting how task-relevant scene states may evolve under interaction.<n>Latent transitions are commonly realized with Transformer-based predictors whose inductive structure is centered on token interaction rather than temporal evolution.<n>We introduce the Latent Evolution Operator Network (LEON), which models latent evolution in a learned observable space through context-modulated operator-based propagation and additive forcing.
• Score: 6.732084106610505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: World Action Models (WAMs) augment robot policies by predicting how task-relevant scene states may evolve under interaction. Recent WAMs increasingly perform such prediction in latent representation spaces, avoiding full appearance-level generation while preserving control-relevant information. Yet latent transitions are commonly realized with Transformer-based predictors whose inductive structure is centered on token interaction rather than temporal evolution. We study transition realization as an architectural choice distinct from predictive representation and prediction-policy coupling. We introduce the Latent Evolution Operator Network (LEON), which models latent evolution in a learned observable space through context-modulated operator-based propagation and additive forcing. Grounded in the controlled Koopman generator view of evolution, LEON organizes context-dependent transition variation around a shared evolution-operator structure while retaining a complementary path for additive change. Controlled dynamical systems verify the resulting evolution-specific inductive bias and the complementary roles of operator propagation and forcing. Across two WAM formulations that integrate latent prediction into the policy differently, LEON improves closed-loop performance and robustness while remaining effective under full transition replacement. These results establish transition realization as a consequential architectural choice in latent WAMs.

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