p class="paper-summary">We present MAGI-1, a world model that generates videos by autoregressively
predicting a sequence of video chunks, defined as fixed-length segments of
consecutive frames. Trained to denoise per-chunk noise that increases
monotonically over time, MAGI-1 enables causal temporal modeling and naturally
supports streaming generation. It achieves strong performance on image-to-video
(I2V) tasks conditioned on text instructions, providing high temporal
consistency and scalability, which are 