[vvolhejn]

Václav here from the team, we're happy to answer questions :) The most surprising part to me is the auto-recovery behavior we mention at the end of the blog post, since any other model I've seen always stays diverged once it goes off the rails once. But MIRA really doesn't like to be out-of-distribution. To be completely honest we're not entirely sure why this happens.

[blackfireWM]

I'm insanely impressed with MIRA! I mean WOW, I've been doing a lot of diffusion world model experiments and I had thought of trying stuff like "visual anchors" to support some form of long form continuity but you guys really blew the field out of the water. My intuition(i've still yet to fully read the research paper i apologize) is that feeding in 4 highly dynamic(yet correlated, due to focusing on the ball) perspectives for each frame is what gives the incredible coherence. As said in the blog and paper, essentially infinite rollout. Thank you, and all of the team, for making your work public! I'm not above begging for an internship just to be clear https://github.com/blackfirebitcoin/hyrule-dreamer-wm

[shay_ker]

Great work! My question is about what you mentioned at the end - how well do world models operate when out of distribution? In some sense we hope these models learn something "deeper" about how the world works and can apply that knowledge to different tasks.

I saw lots of awesome ablations in the paper (loved it!), but I'm curious if you analyzed the latents to get an intuition for what the model actually learned. Or, is it just that it learned the training data distribution really, really well?