Thrilled to share CausalWM, our causal world model v1 built based on Causal Chain-of-Thought (CoT). It currently ranks No. 1 on TriWorldBench and the robot domain of PAI-Bench. Many world models predict the future frames directly from current observations with language or action conditions. This can produce visually convincing videos at first glance, while still getting many subtle but important physical details wrong. Because the underlying physical variables remain entangled in implicit representations, it is difficult to tell whether the model has truly captured the relevant physical mechanisms or is merely relying on shortcuts that produce plausible-looking results. With CausalWM, we bring Causal CoT into future video prediction. The model follows an explicit sequence: Optical Flow → Geometry / Pointmap → Future RGB Each step in the causal CoT captures a meaningful intermediate transition, allowing the model to progressively capture causal dependencies underlying physical evolution. One result I find especially interesting is the in-context control interface created by Causal CoT. Because our CausalWM has learned to use new visual signals in causal CoT. With limited fine-tuning, any useful features (e.g., simulator-rendered robot trajectories) can be added as in-context control, to guide it to generate consistent future videos. This in-context learning ability gives us a practical way for physical reasoning and causal intervention. Links in the thread below.