WALL-SS: Scaling Long-horizon World Models via Next-Scale Autoregression

Source: arXiv cs.RO·2026-08-28 12:00Publish time unverified

arXiv:2608.26239v1 Announce Type: new Abstract: Generative world models provide robots with predictive models of how the world evolves under interaction, with growing potential for simulation, planning, policy evaluation, and robot learning. Beyond clip-level future prediction, a unified generative formulation should relate actions to consequences, support flexible horizons and continuous interaction, and enable reward-driven optimization.