#video generation

LingBot-Video is a 13B parameter sparse-MoE video diffusion transformer (1.4B active) that has been post-trained with reinforcement learning using six rewards, including a physical-plausibility reward, and supports action-conditioned world model prediction for robot rollouts. This work advances video generation and world modeling by combining sparse MoE for efficiency, RL post-training for improved plausibility, and open release of weights and code, making it a strong candidate for robotics and video synthesis research. The model uses a DeepSeek-V3-style sparse MoE with 128 experts and top-8 routing, achieving top average performance on RBench but ranking second on general text-to-video. The physical-plausibility reward is judged by a VLM, which raises concerns about reward hacking despite adding real-video negatives.

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.

Google DeepMind's Gemini Omni Flash model achieved a score of 1404 points on the Video Arena blind test leaderboard, surpassing ByteDance's Seedance 2.0 Mini by 101 points. This milestone marks a significant shift in AI video generation ranking, demonstrating Google's competitive edge over ByteDance in the rapidly evolving field of generative AI video. Video Arena rankings are based on anonymous user votes, and Google's video model ranking has improved by 7 positions compared to the Veo series era.