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A pair of papers argues the field of interactive video AI is forking: one track pushes world models toward game-engine-like simulators with explicit state, the other reframes video as a persistent world plus a stream of events for real-time interacti
Watch the film first The film is the fast version of this argument. It starts with the...
Three new open research projects point the same way: instead of only learning what to do, agents are learning to simulate the environment itself, so they can practice in their own imagination.
A new benchmark tests whether video AI systems can track what happens to parts of a scene the camera isn't currently showing. Across 23 models, the answer is mostly no — and making the models larger made the problem worse, not better.
AI video models that are supposed to "understand" a 3D scene only remember what's on screen — pan away and back, and things have reset. Bigger models are worse at it.
Researchers showed that when a world-model AI imagines impossible futures, it's usually in places it barely saw in training - and that you can predict and fix those blind spots cheaply.
A sweeping study of dozens of AI video-prediction systems finds they don't truly remember the world; when something leaves the frame, they quietly reinvent it the next time you look.
Generating a full imagined video of what comes next is expensive. A new method skips it — pulling a robot's next move straight from the inner workings of an image-editing model.
The common belief is that a robot needs to imagine a video of what happens next to plan. A new method says no — imagine a single still frame, and don't even fully draw it.
PhysiFormer forecasts physical motion as real 3D meshes in space - and recovers rigidity and momentum without anyone hand-coding the laws of physics.
Instead of building an ever-deeper neural network to simulate the future, a new design re-runs one small block over and over — doing comparable work with a fraction of the size.
Qwen-AgentWorld trains a model to simulate the environment an agent acts in, then uses that simulation as a cheap, controllable place to learn -- reporting gains beyond training in the real thing.