4 papers indexed
Yi-Ge Zhang, Tianqi Du, Qi Zhang, Yisen Wang
Latent world models underpin much of modern model-based control, yet current action-conditioned formulations supervise the next-latent transition with a single, undifferentiated target, forcing a monolithic learning signal to absorb every source of state change. In real world, ho…
Lizhe Fang, Weizhou Shen, Tianyi Tang, Yisen Wang
Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an important frontier. However, we identify a critical failure mode in this regime: \emph{repetitive cop…
Letian Cheng, Qi Zhang, Yisen Wang
Latent world models have emerged as a powerful planning paradigm by learning action-conditioned predictive dynamics and using them as internal simulators to imagine and evaluate candidate action sequences. However, as the planning horizon grows, performance becomes increasingly c…
Jingyi Cui, Qi Zhang, Hongwei Wen, Yisen Wang
Joint Embedding Predictive Architectures (JEPAs) have recently emerged as a promising paradigm for world modeling by learning predictive dynamics in a latent space rather than generating future observations at the input level. Despite their empirical success, the theoretical unde…