Title: Autoregressive Graph Network for Learning Multi-step Physics. Abstract: In this work, we propose an Autoregressive Graph Network~(AGN) that learns forward physics using a temporal inductive bias. Currently, temporal state space information is provided as additional input to a GN when generating roll-out physics simulations. While this relatively increases the network's predictive performance over multiple time steps, a temporal model enables the network to induce and learn temporal biases. In dynamical systems, the arrow of time simplifies possible interactions in the sense that we can assume current observations to be dependent on preceding states. The autoregressive property naturally induces the arrow of time and can further constrain physics-induced GNs to conserve symmetries over long time-steps. Our proposed GN encodes temporal state information using an autoregressive encoder that can parallelly compute latent temporal embeddings over multiple time steps during a single forward pass. We perform case studies that compare multi-step forward predictions against baseline data-driven GNs across diverse datasets that feature different particle interactions. Our approach outperforms the state of the art GN and physics-induced GNs in 9 out of 10 and in 7 out of 10 particle physics datasets when conditioned on optimal historical states. 