#drift reduction

Researchers at LingBot World have released an open-weights interactive world model that uses a MoBA attention mask (mixing bidirectional and autoregressive attention) and post-training distillation over long self-rollout trajectories to significantly reduce drift, demonstrated by a continuous 60-minute stable rollout. This work addresses a critical challenge in interactive world models—drift accumulation during long rollouts—which has limited their practical usability. The combination of MoBA attention and long-rollout distillation could enable more stable and reliable interactive simulations for applications like video games, robotics, and virtual environments. The MoBA attention mask dynamically schedules KV-cache to keep long rollouts tractable, and the model uses Plücker embeddings plus AdaLN for camera control. A limitation is that persistence is only in appearance, not identity, meaning regions leaving the context window are regenerated on revisit, not recalled. The weights are released under CC-BY-NC-SA license.