斯坦福团队推出LEON网络，用算子结构改进机器人预测模型，比传统Transformer更关注时间演化。
研究人员提出LEON网络，用于建模世界行动模型(WAM)中的潜在演化。该网络通过上下文调制算子传播和加性强制，在可观测空间中实现演化控制。在两种WAM架构中，LEON均提升了闭环性能和鲁棒性，即使在完全转换替换情况下仍保持有效。实验验证了演化特定归纳偏差以及算子传播和强制作用的互补角色。
Making Latent Evolution Explicit: Operator-Structured Transitions for World Action Models
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.