LingBot-Video: sparse-MoE video diffusion transformer (13B total, 1.4B active) post-trained as an action-conditioned world model[R]

Single-stream diffusion transformer with a DeepSeek-V3-style sparse MoE (128 experts, top-8 routing, 1.4B active of 13B total). Six-reward RL post-training including a physical-plausibility reward, plus an action-to-video mode that predicts robot rollouts from action and hand-pose conditions. Weights, code, and a Diffusers/SGLang stack are open under the LingBot-Video name.

Two things I would push on, and would genuinely like this sub’s read:

1. The physical-plausibility reward is graded by a VLM from sampled frames. Is a VLM a defensible judge of physics, or is that Goodhart waiting to happen? (They do add real-video negatives to fight reward hacking.)
2. It is framed as a policy evaluator and action planner, but every result is video-frame quality with no closed-loop robot numbers. Where is the line between a video generator and a world model?

On RBench it posts the top average, though the reasoning-heavy dimensions still go to a closed model, and it is only second on general T2V in their own eval. Please tear it apart.

Paper, code, and weights: https://technology.robbyant.com/lingbot-video , https://github.com/robbyant/lingbot-video , https://huggingface.co/robbyant/lingbot-video