[olivercameron]

If I had to choose one, I'd easily say maintaining video coherence over long periods of time. The typical failure case of world models that's attempting to generate diverse pixels (i.e. beyond a single video game) is that they degrade to a mush of incoherent pixels after 10-20 seconds of video.

We talk about this challenge in our blog post here (https://odyssey.world/introducing-interactive-video). There's specifics in there on how we improved coherence for this production model, and our work to improve this further with our next-gen model. I'm really proud of our work here!

> Compared to language, image, or video models, world models are still nascent—especially those that run in real-time. One of the biggest challenges is that world models require autoregressive modeling, predicting future state based on previous state. This means the generated outputs are fed back into the context of the model. In language, this is less of an issue due to its more bounded state space. But in world models—with a far higher-dimensional state—it can lead to instability, as the model drifts outside the support of its training distribution. This is particularly true of real-time models, which have less capacity to model complex latent dynamics. Improving this is an area of research we're deeply invested in.

In second place would absolutely be model optimization to hit real-time. That's a gnarly problem, where you're delicately balancing model intelligence, resolution, and frame-rate.