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Video might be the next intelligence substrate. Strikingly, video models are beginning to exhibit the same emergent reasoning behaviors first observed in LLMs—multi-path search, self-correction, and layer specialization. We demystify video reasoning and show it doesn’t happen frame-by-frame, but along diffusion steps. 🔗 📄 So, what's next? ;)
71,036 просмотров • 6 месяцев назад •via X (Twitter)
Комментарии: 15

It is well known that x0 prediction in early timestep represents the weighted average of all possible outcomes, but I feel it’s misleading to call it “reasoning”. How is “reasoning” defined here?

Great point — we don’t consider the presence of multiple possible outcomes at the start to be reasoning. Rather, we view reasoning as the subsequent selection process: early steps maintain multiple candidates, and later steps progressively refine and prune them toward a consistent solution. In that sense, it is analogous to latent reasoning in LLMs—except that here the process is more directly observable through the diffusion trajectory.

Video reasoning feels like magic until you see the compute bill.

Don’t worry, one year ago I thought video models could never be accelerated to run in real time. Now guess what ;)

@kellypeilinchan

@kellypeilinchan This is already happening ;)

Interesting

Who knew “compression” could turn into AGI haha

A picture is worth a thousand words

True, and I wonder if words are necessary at all in many visual reasoning scenarios

@CritterObserves @caizhongang for video QA (even for very long videos), we proved here that they can be done end to end on visuals losslessly (without intermediate captioning at all) + logarithmic compute:

@CritterObserves Interesting work!

This seems to be like a natural result of bidirectional attention and diffusion; would like to know the observation on AR video gen model, e.g., diffusion forcing?

Great idea!

Our GitHub repository is now live for discussions and upcoming tool releases. Follow us for the latest updates! GitHub:

