Author

Yadan Luo

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Preprint Aug 2026

Flow-JEPA: Robust Latent Dynamics for JEPA World Models via Flow Matching

Joint-Embedding Predictive Architectures (JEPAs) provide a powerful framework for latent world modeling and planning in a reconstruction-free manner. Although numerous JEPA-based approaches have been proposed to mitigate representation collapse, our experiments on localized, out-of-distribution visual noise reveal that...

Yan-Chen Huo, Zi-Ying Song, Yadan Luo · 0 citations

Preprint Aug 2026

MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving

MomADv2, a reliable state-space memory framework for long-horizon end-to-end autonomous driving, introduces a Selective State-Space Planning Memory Query Module, which filters historical planning queries based on temporal continuity and command consistency, and models the evolution of planning intentions through a sele...

Zi-Ying Song, Sheng-Kai Zhang, Lin Liu et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Flow-JEPA: Flow Matching for Robust Latent Dynamics in JEPA World Models

This work proposes Flow-JEPA (F-JEPA), a conditional flow matching dynamics model that jointly generates a sequence of future latent states conditioned on the current observation and actions, suggesting that conditional flow matching provides a promising alternative to deterministic autoregressive dynamics in JEPA worl...

Yan-Chen Huo, Zi-Ying Song, Yadan Luo · 0 citations