Very cool work, the learned world state is a smart way of getting consistent generation across all the views (and not having the map vanish when you 180 like some other models). Multi-agent is such an interesting field, because it's clear that humanity benefits from distributed intelligence, but I don't think MARL has really had a big breakthrough like AlphaGo or RLVR for single-agent RL.

Two thoughts about where this could go: first, the internal world state would need to be learned to transfer to real-life robotics, since you can't query the internals of a game engine in training. Second, an enormous challenge for many of these world models is going to be truly unbounded environmental interactivity - Agora is still mostly about a few agents interacting in a static environment. Learning interaction will be hard, because the interactions in games are intentionally added in, by hand. But we (human learners) acquire a strong model for environental interaction very efficiently, which is part of what helps us generalise so effectively.