Unverified50% confidenceFactExact time
An autoregressive world model refers to a generative model that takes the current or historical state as input and predicts future states step by step, commonly treating the scene representation at each moment as a token sequence and performing inference in temporal order with structures such as Transformers.
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Long-term
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10/16/2026
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VerifiedWorld models are essentially learned dynamical equations of an environment—given the current state and action, they predict the next state.76% similarUnverifiedAn ideal world model predicts the next state given the current state and action; this next-frame prediction capability has commonalities with video generation.74% similarUnverifiedModern systems for arrival time prediction incorporate probabilistic forecasting models, such as Kalman filtering for real-time state estimation or machine learning models based on historical data.73% similarUnverifiedPrediction capability is typically implemented by learning a transition model: given the current state and action, the model predicts the next state.73% similarUnverified大模型推理的本质是自回归生成，即不断预测下一个token71% similar
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