VLAs vs Nvidia world models vs all of that: where are we going?

I'm reading the papers of Cosmos3 and Dreamzero and they looks very promising (compared to memoryless VLAs). And I am wondering where the filed will evolve. 

Based on your practical experience with new models, what's your bet between VLAs, WM, Jepa-style, WAM, and all of that? 

I worked so far with VLAs (eg pi05), and I don't have any experience in using the nvidia stack so far, of and other world action models. I am thinking if I should invest time in changing the base policy, and I'd appreciate some feedback form who has tested them (ie: the open source/weights model available, and capable of inference without one thousand gb of vram) 

On my side I'm a fan of model working planning latent space; video action models (which have more temporal coherence wrt vla), but I also feel that semantic power of a VLM should be present aswell.

Ps: suggested survey reading in this topic:

"World Model for Robot Learning: A Comprehensive Survey"

Happy to discuss with you