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This is cute and retro! But I think training only on GoldenEye undersells the concept a bit, since their world model inherits the N64-era graphics from GoldenEye, which automatically makes it look dated.

If they retrained the same model on real video data, they could potentially get a multiplayer world with quite realistic-looking graphics (see https://wayve.ai/wp-content/uploads/2025/11/Ex-2-GAIA-3.mp4, https://wayve.ai/wp-content/uploads/2025/11/Ex-3-GAIA-3.mp4).

Maybe for Agora-2 :)

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.

Underwhelming demo. Also the controls are terrible, but, the real Goldeneye was also underwhelming with bad controls if you had played Quake II.

Unlike LLMs which made it into the public view, I have a hard time seeing these world simulation models doing the same

I'm not sure how to imagine their use in education or gaming, but it's clear that they have a real potential for being used in military programs

It's nightmarish to think these could be trained on shooting game footage and thrown into real life scenarios in some form or another

Hmm. Neat ( especially prowl -- as an idea ). But.. I don't see anything beyond the game. I might be a little cautious, but there is no way for me test any of it ( and I was actually setting up an old mmo this weekend to see how well agent can survive within a rigid ecosystem ). Is it just intended for pure researchers or something?

I played the game - the inputs feel like trash... I'm not convinced this is the correct direction to generate games. We should probably only be generating scripts and assets to plug into game engines, rather than relying on GenAI for the actual engine.

Gamepads usage doesn't match screen actions/movements through the entire presentation video(s), zero trust at this point.

Edit: I apologize, entirely missed the game itself, partially understand the mismatch now, but still, it's much heavier in the presentation than in real life for some reason

Is there a little bit more on this in terms of evaluation or is this rather a Show-HN post?

Be careful when transposing game-learned behaviors into real life.

I'm confused how the world itself works - or would work - at scale. Taking that minecraft clip on the link, if I jump down a cave first, and the model decides the cave goes off to the left. A few hours later I come back, and this time the model decides the cave bends to the right?

Can't wait for a fully immersive game with npcs

good step in a direction

Super cool!

we have this before gta-6