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arXiv Machine Learning By Xiaoxiao Lu, Yunlong Dong, Jiahao Shi, Ye Yuan

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

Read the original on arXiv Machine Learning →

The paper introduces the Latent Evolution Operator Network (LEON), a new architecture for modeling latent state transitions in World Action Models (WAMs). LEON uses a learned observable space with context‑modulated operator‑based propagation and additive forcing, grounded in a controlled Koopman generator framework. Experiments on two WAM formulations show that LEON improves closed‑loop performance and robustness, demonstrating that the way transitions are realized is a crucial architectural choice in latent WAMs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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